Federated Edge Learning (FEL) enables privacy-preserving, on-device training across heterogeneous edge devices, reducing data transfer costs. However, most FEL approaches remain simulation-based and fail to address realistic on-device training asynchrony caused by variations in computing power, data volume, and energy availability among devices. To address the issue, we proposeSPFERE, aSemi-synchronousPower-aware andFairnEss-RegulatedEngine in this paper, designed for power-constrained edge environments and implemented on a real-world edge testbed to support asynchronous model updating, power management, and fairness-aware model aggregation. Specifically, we propose a client grouping-based semi-synchronous aggregation protocol that reduces idle waiting time for power-abundant devices and mitigates stale updates from power-constrained devices, along with our in-depth convergence analysis. Then, we introduce a long short-term memory (LSTM)-based power estimation approach to predict remaining battery voltage for devices with limited communication overhead, enabling early warnings for power dropouts. Lastly, we design fusion-based fairness-aware model aggregation methods to prevent bias by considering device participation frequency and training workload. We systematically validate our framework through experiments on both a simulation platform and a real-device testbed. Our extensive experimental results demonstrate the effectiveness and resilience ofSPFEREin dynamic and heterogeneous edge environments.

Remote sensing image fusion aims to reconstruct a high spatial and spectral resolution image by integrating the spatial and spectral information from multiple remote sensing sensor data. Despite the remarkable progress of deep learning-based fusion methods, most existing methods rely on manual network architecture design and hyperparameter tuning, lacking sufficient interpretability and adaptability. To address this limitation, we propose a novel neural Ordinary Differential Equation (ODE)-inspired tuning-free proximal splitting algorithm, which splits remote sensing image fusion as two optimization problems regularized by deep priors to model the fusion of spatial and spectral. Firstly, based on the physical properties of spatial and spectral information, the two problems are optimized by two proximal splitting operators to iteratively integrate spatial-spectral complementary information, eliminating or suppressing redundant information to reduce fusion errors. Secondly, considering the efficiency of neural ODE in reducing optimization error, we utilize a high-order numerical scheme to customize the proximal operator theoretically without additional handcrafted design and parameter tuning. Finally, by incorporating the numerical scheme as a solver into the proximal optimization algorithm, we derive an ODE-inspired Tuning-free Proximal Network, dubbed OTPNet, which achieves efficient and robust fusion reconstruction. Extensive experiments on nine datasets across three different remote sensing image fusion tasks show that our OTPNet outperforms existing state-of-the-art approaches, which validates the effectiveness of our method.

In WiFi and many other sensing applications, researchers have adopted the approach of representing each activity class with a small portion of data to address user variability across different environments, thereby enabling model generalization. This solution raises yet another challenge - limited training samples for each activity class, particularly for a considerable number of classes to be classified. In this paper, we first introduce a visionary architectural layering architecture that enables the end-to-end lifecycle of multi-sensor federated learning, paving the way for realizing federated AI-based sensing. We identify three challenges, e.g., data heterogeneity, scalability under non-IID conditions, and few-shot learning. According to the architecture, we showcase a lightweight, general workflow that utilizes a federated learning framework to address the few-shot learning challenge across various classifications in WiFi Channel State Information (CSI). We analyze this workflow and identify two key factors that most significantly affect its performance (i.e., the aggregation method and the model architecture). Based on the analysis, we propose three network models for CSI-based activity recognition: CSI-AlexNet, CSI-ActNet, and CSI-ResNet. Through extensive performance evaluations, our experimental results show that the proposed approach achieves an accuracy of 97.97% on CSI-ActNet while maintaining low computational demands, making it well-suited for real-time fine-tuning on edge devices with constrained resources.

The swift expansion of integrated renewable energy within the electrical grid underscores the essential role of distributed energy resources (DERs) in shaping the future of power transmission and distribution networks. This increasing incorporation of DERs has prompted power utility providers to enhance their systematic awareness and deploy advanced load control techniques. Nonetheless, a persistent challenge revolves around optimizing energy dispatching amidst substantial fluctuations originating from DERs. These fluctuations primarily stem from long-term data stochasticity and the inherent unpredictability of integrated DERs. Existing methods fall short in addressing the intricate nature of energy distribution from utilities to households, especially when dealing with exogenous loads beyond smart meters. To address this issue, this paper proposes RoGANDER, a Robust Generative Adversarial Network (GAN) for DERs. It overcomes challenges associated with high spatial and temporal data granularity, along with significant time consumption. The RoGANDER approach leverages generative sequential data to mitigate data unpredictability and stochasticity. Our evaluation results confirm a notable reduction in fluctuations and an enhanced ability to anticipate households’ energy needs.

The increasing challenges caused by contaminated soil and frequent climate changes have significantly impacted traditional agricultural systems. Indoor aquaponic systems, which utilize nutrient-rich fish tank water to cultivate plants, present a viable solution by eliminating the need for soil and providing a controlled environment that mitigates the effects of climate change. However, maintaining a balanced nutrient composition for fish and plants remains challenging. Integrating the Internet of Things (IoT) into aquaponic systems enables realtime monitoring of nutrient levels, ensuring optimal system maintenance. In this paper, we present the development of a portable and low-cost Aquaponic Monitoring System designed to automate nutrient data collection, reducing the need for manual labor while enhancing system efficiency. This system not only addresses the current challenges but also opens up exciting possibilities for further optimization, ultimately increasing yield and sustainability in aquaponic farming.

The fast development of smart cities includes edging technologies such as Digital Twin (DT) that plays a critical role in developing and managing smart cities by providing dynamic, virtual representations of physical assets, systems, and processes. These digital replicas are continuously updated with real-time data collected from Internet of Things (IoT) devices, such as sensors, smart devices, connected vehicles, and monitoring systems, to enable city planners, policymakers, and operators to monitor, analyze, and optimize urban environments with unprecedented precision. To gain meaningful insights from collected data, it is necessary to have robust database systems capable of efficiently storing, organizing, and retrieving large volumes of information. Databases serve as the backbone of smart city applications, ensuring that IoT-generated data can be accessed quickly and reliably to support analytics, predictive modeling, and real-time operations. This paper examines MongoDB, a widely used nonrelational database technology, with the objective of exploring various database architectures and features. The analysis focuses on how these architectural choices influence the performance of smart city applications, providing insights into the factors that should be considered when selecting a database solution for IoTdriven environments.

In this paper, we address the issue of Channel State Information (CSI) prediction of the Internet of Vehicles (loV) system, which is a highly dynamic network environment. We propose a deep reinforcement learning-based approach to predict CSI with historical data and video footage captured by smart cameras. Specifically, we use a Conventional Neural Network (CNN) to extract unique environmental characteristics, which will be sent to a Recurrent Neural Network (RNN)-based learning model so that the future CSI can be predicted. Our approach also considers the heterogeneous nature of IoV communication environments by adopting transfer learning to reduce the training cost when applying our approach to different IoV scenarios. We assess the efficacy of our proposed approach using our designed IoV simulation platform. The experimental results confirm that our approach can accurately predict CSI by using historically generated data.

Object detection is significant in real-world applications, including self-driving cars, surveillance systems, and visionenabled robotic systems, among others. Despite the success of benchmark deep learning-based approaches, like YOLO, achieving high detection accuracy, they are typically computationally intensive and require GPUs to achieve optimal performance, preventing them from being widely deployed on low-power enduser devices. Particularly, when deploying these models on non-GPU devices, their inference speed is significantly degraded due to the lack of GPU support. To this end, in this paper, we propose an optimized object detection model called QATFP-YOLO (Quantization-Aware Training and Filter Pruning on YOLO), aiming to enhance inference speed on non-GPU devices, which could be trained and inferred on local end-user devices without GPU support. To reduce the computation complexity, we propose two optimized training strategies based on our QATFP-YOLO model by considering: (i) model quantization technique that reduces the model size and memory usage without sacrificing accuracy and (ii) filter pruning technique that removes redundant parameters from the model, further reducing memory usage and inference time. By evaluating the performance on a real smartphone, we find our QATFP-YOLO model achieves exceptional inference speeds, reaching approximately 88 frames per second, notably surpassing traditional YOLO-Lite models by over fourfold.

The Internet of Vehicles (IoV), as one subset of the Internet of Things (IoT) in the smart transportation area, integrates vehicle networks with sensors and actuators. By connecting all sensors to the network, the IoV enables smart transportation (i.e., autonomous vehicles) and makes smart cities a reality. In smart transportation systems, roadside units (RSUs) capture all vehicle information and serve as gateways. However, smart transportation infrastructure has yet to mature in the current stage. RSUs are insufficient to support all vehicles. Meanwhile, the low computational capability of vehicles makes it challenging to recompute the driving route as the road environment changes. To address the problem of insufficient RSU coverage, one protocol called IEEE 802.11p enables vehicle-to-vehicle communication using relays. Nonetheless, data transfer among vehicles via relays is still time-consuming for a large-scale transportation network. To deal with the above issues, in this paper, we propose an IoV framework using digital twins (DTs) to digitize the IoV environment and assign nearby IoT gateways compatible with the RSU communication protocol. This framework lets DTs update the vehicle’s driving route based on real-time information. With a case study, we evaluate the efficacy of DT-assisted IoV based on communication latency and vehicle driving efficiency. Our evaluation results confirm that the proposed framework can efficiently enhance communication latency when the relay needs to pass through two or more vehicles and reduce travel time when vehicles receive updated route information at intersections.

Internet of Things (IoT) devices generate massive amounts of data from local devices, making Federated Learning (FL) a viable distributed machine learning paradigm to learn a global model while keeping private data locally in various IoT systems. However, recent studies show that FL’s decentralized nature makes it susceptible to backdoor attacks. Existing defenses like robust aggregation defenses have reduced attack success rates by identifying significant statistical differences between normal and backdoored models individually. However, these defenses fail to consider the potential collusion among attackers to bypass statistical measures utilized in defenses. In this paper, we propose a novel attack approach, called collusive backdoor attacks (CBA), which bypasses robust aggregation defense by considering both local backdoor training and post-training model manipulations among collusive attackers. Particularly, we introduce a non-trivial perturbation estimation scheme to add manipulations over model update vectors after local backdoor training and use the Gram-Schmidt process to speed up the estimation process. This makes the magnitude of the perturbed poisoned model to the same level as normal models, evading robust aggregation-based defense while maintaining attack efficacy. After that, we provide a pilot study to verify the feasibility of our perturbation estimation scheme, followed by its convergence analysis. By evaluating the attack performance on four representative datasets, our CBA approach maintains high attack success rates under benchmark robust aggregation defenses in both IID (independent and identically distributed) and non-IID local data settings. Particularly, it increases the attack success rate by 126% on average compared to individual backdoor attacks.

Federated learning (FL) allows the collaborative training of machine learning (ML) models between an aggregation server and different clients without sharing their private data. However, the FL archetype is mostly vulnerable to malicious model updates from various clients because of the privacy feature that makes the server see clients as a black box. When selecting clients, the server has no control on their contributions during training. This opacity of the server toward clients’ data associated with the huge amount of heterogeneous data brings a security risk and poses a deterioration of the model performance in FL. The impact of client selection and data heterogeneity on FL robustness has been overlooked. In this article, we develop an incentive design for heterogeneous client selection (IHCS) to improve the performance while reducing the security risks in FL. The IHCS approach applies a smarter client selection method using cooperative game theory and dynamic clustering of clients based on their heterogeneity level to overcome the challenges of lacking access to clients’ data, unbalanced data, and the lack of applicable data contribution from clients in FL. To do so, we attribute a recognition value to each client using the Shapley value. This recognition index is then used to aggregate the probability of participation level. We also implement, within the IHCS, a heterogeneity-based clustering (HIC) method that helps inhibit the negative influence of data heterogeneity and increase client contributions. Through extensive experiments with empirical results, the proposed approach outperforms the representative works on robustness of FL.

With the proliferation of computationally powerful edge devices, edge computing has been widely adopted for wide-ranging computational tasks. Among these, edge artificial intelligence (AI) has become a new trend, allowing local devices to work cooperatively and build deep learning models. Federated learning is one of the representative frameworks in distributed machine learning paradigms. However, there are several major concerns with existing federated learning paradigms. Existing distributed frameworks rely on a central server to coordinate the computing process, where such a central node may raise security concerns. Federated learning also relies on several assumptions/requirements, e.g., the independent and identically distributed (i.i.d.) data and model homogeneity. Since more and more edge devices are able to train lightweight models with local data, such models are normally heterogeneous. To tackle these challenges, in this article, we develop a blockchain-empowered federated learning framework that enables learning in a fully decentralized manner while taking the model heterogeneity and data heterogeneity into account. In particular, a federated learning framework with a heterogeneous calibration process, i.e., Model and Feature Calibration (FL-MFC), is developed to enable collaboration among heterogeneous models. We further design a two-level mining process using blockchain to enable the secure decentralized learning process. Experimental results show that our proposed system achieves effective learning performance under a fully heterogeneous environment.

Existing makeup transfer methods typically transfer simple makeup colors in a well-conditioned face image and fail to handle makeup style details (e.g., complicated colors and shapes) and facial occlusion. To address these problems, this paper proposes Hybrid Transformers with Attention-guided Spatial Embeddings (named HT-ASE) for makeup transfer and removal. Specifically, a makeup context extractor adopts makeup context global-local interactions to aggregate the high-level context and low-level detail features of the makeup styles, which obtains the context-aware makeup features that encode the complicated colors and shapes of the makeup styles. A face identity extractor adopts a face identity local interaction to aggregate the identity-relevant features of shallow layers into identity semantic features, which refines the identity features. A spatially similarity-aware fusion network introduces a spatially-adaptive layer-instance normalization with attention-guided spatial embeddings to perform semantic alignment and fusion between the makeup and identity features, yielding precise and robust transfer results even with large spatial misalignment and facial occlusion. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods, especially in the preservation of makeup style details and handling facial occlusion.

Moving object trajectory tracking plays an important role in traffic scheduling, route planning, advertising recommendations, and other associated social services. The success of moving object trajectory tracking can be attributed to the extensive use of Internet of Things (IoT) devices, which collect a growing volume of spatio-temporal data. To mine the spatio-temporal correlation, traditionally, recurrent neural networks (RNNs) and their variants, such as long-short-term memory (LSTM) and bidirectional long-short-term memory (BiLSTM), have shown their effectiveness in forecasting moving object positions. However, these methods have faced challenges in dealing with complex temporal dependencies due to the limited memory of storing past information using basic hidden layers. To address this issue, in this study, we propose a spatiotemporal attention-based transformer model to mine the spatiotemporal correlation of moving object trajectories in smart transportation systems, which offers improved performance in long-term trajectory prediction tasks. Moreover, most existing works overlook the importance of sampling issues in the trajectory prediction and tracking process. To this end, we develop an adaptive approach by leveraging spatio-temporal sampling to optimize trajectory tracking with reduced data transmission rates and computational costs. The experimental results on real-world datasets demonstrate the superiority of our transformer-based approach over existing RNN-based methods in trajectory predictions and confirm the feasibility of our optimal sampling solution in enhancing trajectory tracking performance.

$A$smart transportation system (i.e., intelligent transportation system) refers to a transportation critical infrastructure system that integrates advanced technologies (e.g., networking, distributed computing, big data analytics, etc.) to improve the efficiency, safety, and sustainability of the transportation system. However, the rapid increase in the number of vehicles on roads and significant fluctuations in the flow of traffic can cause the coverage holes of Road Side Units (RSUs) and local traffic overload in smart transportation systems, which can negatively affect the performance of systems and causes accidents. To address these issues, deploying Unmanned Aerial Vehicles (UAVs) as mobile RSUs is a viable approach. Nonetheless, how to deploy UAVs to the optimal position in the smart transportation system remains an unsolved issue. This paper proposes a Vehicle Trajectory-based Dynamic UAV Deployment Algorithm (VTUDA). The VTUDA utilizes vehicle trajectory prediction information to improve the efficiency of UAV deployment. First, we deploy a distributed Seq2Seq-GRU model to the UAVs and train the model. We leverage the well-trained model to predict vehicle trajectory. VTUDA then uses the predicted information to make informed decisions on the optimal location to position the UAVs. Further-more, VTUDA considers both the condition of communication channels and energy consumption during the deployment process to ensure that UAVs are deployed to optimal positions. Our experimental results confirm that the proposed VTUDA can effectively improve the deployment of UAVs. The experimental results also demonstrate that VTUDA can significantly enhance vehicle access and communication quality between vehicles and UAVs.

The Internet of Things (IoT) paradigm is gradually becoming more prevalent through numerous devices and technologies, including sensors, actuators, microcontrollers, cloud-enabled services, and analytics. IoT objects gain intelligence by integrating with wireless sensor networks (WSNs), mobile computing and communication, and others. With sensors, smart things can be enabled by monitoring and identifying environmental changes related to motion, temperature, humidity, pressure, light, vibration, etc. To timely keep track of state changes, researchers are considering developing a cyber replicator, denoted as Digital Twin (DT), of real physical systems as a way to visualize, model, and work with complex cyber-physical systems (CPS). In this paper, we first refine the dataset to a format that can be easily used for deep learning (DL) experiments, IoT data pipeline development, data modeling and simulation, data aggregation, etc. We then demonstrate that DT data can be used to determine space occupancy based on the ambient light sensor, which tends to indicate occupancy in particular spaces because the building has smart lighting that will switch off when rooms are unoccupied after a certain time. Given the apparent developments in machine learning technology, it is clear that machine learning-based prediction has the ability to enhance resource utilization and further forecast future events. Particularly, we use a DT-based dataset and Long-Short-Term Memory (LSTM) neural network architecture to forecast the campus building’s internal temperature.

Facial recognition (FR) systems are employed to identify and authenticate individuals. There has been a rise in privacy concerns regarding mass surveillance and unauthorized usages. As a result, one viable approach is adding adversarial noise to distort user profile images so that FR technology can be bypassed. Nonetheless, such approaches could be used by adversaries to avoid detection in surveillance footage and therefore evade identification. To combat this threat, a line of research efforts focuses on generative adversarial network (GAN)-based Denoising and Restoration to remove adversarial noise. In this paper, GAN-based methods are investigated experimentally for assessing their effectiveness. Particularly, three GAN-based approaches, i.e., Blind Face Restoration, Blur and Restore, and Image-to-image Translation, are extensively examined with several representative classification approaches. Our evaluation results show that GAN denoising schemes could improve image visual quality, but are ineffective to remove perturbations for privacy protection attached by Fawkes or Lowkey. We further discuss some future research directions on image transformation-based approaches, which can potentially improve the effectiveness.

With the rise and growing attention on Digital Twins (DT) as a way to provide integration between the Internet of Things (IoT) and data analytics, so does the need to consider how to address its challenges. To deal with these challenges, Named Data Networking (NDN) can be a possible solution. NDN has been rising in popularity due to its advancements over the traditional TCP/IP Internet architecture. In this paper, our approach begins with the framework that leverages an NDN-based DT architecture for data management. We then design two scenarios that focus on the performance of data querying in a small and large-scale simulated NDN-based DT architecture. Based on the designed scenarios, we conduct the performance evaluation of data query and DT performance to investigate the performance gap and determine whether an action needs to be taken.

The applications of machine learning (ML) in cyber-physical systems (CPS), such as the smart energy grid has increased significantly. While ML technology can be integrated into CPS, the security risk of ML technology has to be considered. In particular, adversarial examples provide inputs to a ML model with intentionally attached perturbations (noise) that could pose the model to make incorrect decisions. Perturbations are expected to be small or marginal so that adversarial examples could be invisible to humans, but can significantly affect the output of ML models. In this paper, we design a taxonomy to provide the problem space for investigating the adversarial example generation techniques based on state-of-the-art literature. We propose a three-dimensional framework containing three dimensions for adversarial attack scenarios (i.e., black-box, white-box, and gray-box), target type, and adversarial examples generation methods (gradient-based, score-based, decision-based, transfer- based, and others). Based on the designed taxonomy, we systematically review the existing research efforts on adversarial ML in representative CPS (i.e., transportation, healthcare, and energy). Furthermore, we provide one case study to demonstrate the impact of adversarial examples of attacks on a smart energy CPS deployment. The results indicate that the accuracy can decrease significantly from 92.62% to 55.42% with a 30% adversarial sample injection. Finally, we discuss potential countermeasures and future research directions for adversarial ML.

In this paper, we address the issue of automating network configurations for dynamic network environments such as the Internet of Vehicles (IoV). Configuring network settings in IoV environments has proven difficult due to their dynamic and self-organizing nature. To address this issue, we propose a deep reinforcement learning-based approach to configure IoV network settings automatically. Specifically, we use a collection of neural networks to convert the observations of a communication environment (channel power gain, cross-channel power gain, etc.) into key features, which are then supplied to a deep$Q$neural network (DQN) as input for training. Afterward, the DQN will select the optimal network configuration for vehicles in the IoV environment. In addition, our approach considers both centralized and distributed training strategies. The centralized training strategy conducts the DQN training process on a roadside server, while the distributed training strategy trains the DQN on vehicles locally. Through our designed IoV simulation platform, we evaluate the efficacy of our proposed approach, demonstrating that it can improve the quality of services (QoS) in the IoV environments concerning reliability, latency, and service satisfaction.

The interconnection and digitization of the physical world has increased dramatically with the widespread deployment of network communication and the rapid development of the Internet of Things (IoT). Application scenarios and requirements in IoT are more complex and diverse than ever before. To successfully support the design and development of complex IoT systems, a realistic evaluation platform that can accurately simulate both the physical world and network communications is necessary. Yet, most existing simulation tools are limited, simulating only specific subsets of IoT environments, such as communication network simulation or mobility simulation, rather than complete IoT scenarios. Thus, in this paper, we propose a new framework, in which several modules can work together to achieve more realistic simulation of IoT environments. Specifically, we integrate three-dimensional object motion with the OMNET++ network simulator. In our framework, we can configure and direct object movement in 3D and compute the received power of transmitted signals using ray tracing techniques. Within the framework, OMNET++ simulates the communication process based on the received power and communication protocol. As a demonstration of our framework, we conduct several experiments on two classic Internet of Vehicles (IoV) scenarios. The results indicate that our proposed framework can accurately simulate both the physical and communication aspects of IoT systems.

While facial recognition (FR) has been widely used by businesses and governments for various purposes, it gives rise to privacy concerns once the consent of users is not handled properly. Hence, researchers have proposed methods to evade FR technology by attaching adversarial perturbations to user profile images. Nonetheless, image denoising-based methods have been proposed to increase the model robustness over adversarial examples. This paper investigates the impact of transformations on adversarial facial images. In particular, a simple but effective framework, TaD (Transformations as Denoising), is proposed to remove possible adversarial perturbations from user images generated by popular FR privacy protection frameworks. Extensive evaluations show the reliability of Fawkes and LowKey with various simple transformations. Experimental results indicate that simple transformations can impact the protection performance, and the choice of DNN-based facial feature extractors can enhance the robustness of facial images with adversarial perturbations. The experimental results also demonstrate strengths and weaknesses of FR methods and give suggestions for further improvements of privacy safeguard tools.

With the rapid advancement of information tech-nology, the Internet of Things (IoT) has significantly impacted people's daily life. IoT devices not only bring comfort and convenience to every aspect of the world, but also appear to be a new target of cybercrimes. Thus, IoT forensics becomes a critical step in forensics investigation. Intelligent robot vacuums are one of the most popular IoT devices. As robot vacuums can connect to the Internet and be operated through mobile apps, a large amount of data may be stored and transmitted among the vacuums, mobile apps, and the network. The data may include the history of the robot's operation, network and user credentials, and layouts of the floor plan of a house. From the perspective of digital forensics, these data can be critical while collecting necessary evidence, investigating suspects and victims, and reconstructing crime scenes. To this end, this paper makes an initial attempt to conduct a digital forensic analysis on intelligent robot vacuum systems. Specifically, this paper retrieves and analyzes a robot vacuum's operation log, the installation details of the robot vacuum's control system, and the usage record of the application from the memory of a smartphone.

Mobile object tracking, which has broad applications, utilizes a large number of Internet of Things (IoT) devices to identify, record, and share the trajectory information of physical objects. Nonetheless, IoT devices are energy constrained and not feasible for deploying advanced tracking techniques due to significant computing requirements. To address these issues, in this paper, we develop an edge computing-based multivariate time series (EC-MTS) framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks. Specifically, EC-MTS leverages statistical technique (i.e., vector auto regression (VAR)) to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction. Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure. We have validated the efficacy of EC-MTS and our experimental results demonstrate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects. In addition, we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.

The Internet of Things (IoT) encompasses a near-incalculable collection of dispersed and embedded computing devices acting as sensors and actuators, generating data at an incredible scale. However, a lack of coherency and cross-compatibility in IoT deployments has lead to increasing redundancy and waste of resources. To combat this, various concepts have been proposed for an open IoT search engine (IoT-SE) that serves human and machine users. Invariably, the IoT-SE envisions distributed query retrieval to handle massive volumes of devices and data. Incorporating the massively heterogeneous protocols and properties of devices deployed, the search of such a system for timely and pertinent data is massively challenging, to provide useful knowledge and service for IoT systems. Moreover, enabling and maintaining security and privacy in an IoT-SE is likewise a prodigious task, as end users, IoT devices, and the search system itself, have different protocols and requirements. To this end, a study of security issues in IoT search is conducted to outline the challenges ahead, and a case study to resolve practical security vulnerabilities in an IoT-SE system is carried out. The pertinent issues of security in an IoT-SE system are reviewed. Particularly: 1) a taxonomy is detailed for IoT-SE security issues; 2) the vulnerabilities of machine learning (ML) models in the IoT-SE are considered; and 3) defensive mechanisms are presented for securing IoT Search. A case study is carried out to implement basic security features in the IoT search, addressing the risk of false queries through the design of ML-based solutions. Finally, a roadmap for future research is provided, including the security and privacy for IoT systems connected to the IoT-SE, distributed edge computing in IoT-SE, privacy-preserving data markets in IoT-SE, and distributed ML in IoT-SE.

With the increasing adoption of Industrial Internet-of-Things (IIoT) devices, infrastructures, and supporting applications, it is critical to design schemes to effectively allocate resources (e.g., networking, computing, and energy) in IIoT systems, generally formalized as optimization problems. Nonetheless, because the system is highly complex, operation and networking graph-based environments are time varying, and required information may not be available, it is difficult to leverage traditional optimization techniques to solve the optimal resource allocation problem. In this article, we propose a deep$Q$-network (DQN)-based scheme to address both bandwidth utilization and energy efficiency in a networking graph-based IIoT system. In detail, we design a DQN model that consists of two deep neural networks (DNNs) and a$Q$-learning model. The DNN network abstracts the features from the highly dimensional inputs and obtains the approximate$Q$-function for the$Q$-learning model. Based on the$Q$-function, the$Q$-learning model can generate the$Q$-table and reward function. After the training process, the DQN model can select appropriate actions for the agents (i.e., robots in a smart warehouse in this study) to improve bandwidth utilization and energy efficiency. To evaluate our proposed scheme, we design a simulation environment to investigate a typical IIoT scenario: the actuation of robotics in a smart warehouse. We then implement the DQN model and conduct extensive experiments to validate the efficacy of our scheme. Our experimental results confirm that our scheme can improve both bandwidth utilization and energy efficiency, as compared to other representative schemes.

Machine learning, as a viable way of conducting data analytics, has been successfully applied to a number of areas. Nonetheless, the lack of sufficient data is one critical issue for applying machine learning in Industrial Internet of Things (IIoT) systems. Insufficient data raises could negatively affect the accuracy of machine learning models. To tackle this issue, we design a framework to systematically investigate the impacts of insufficient data on model training. This framework employs the generative adversarial network (GAN) and continuous learning to generate and engage new data in model training, enabling us to study the security risks of introducing new data in the model training process and develop countermeasures to mitigate these risks. To validate the efficacy of our framework, we consider a representative IIoT scenario, in which a variety of industrial components needs to be recognized by convolutional neural networks (CNNs), and design and implement three evaluation scenarios that are based on a real-world IIoT data set. Our experimental results confirm that insufficient data can have a significant impact on the model accuracy, but that new data generated by GAN and continuous learning can greatly improve the model accuracy. Our experimental results also show that the data poisoning threat posed by the GAN can significantly reduce the model accuracy. However, our proposed defensive mechanism is capable of securing the model learning process. We conclude this article by discussing some emerging issues that need to be addressed in future work.

The fast development of the Internet of Things (IoT) and deep learning enables learning useful patterns from the massive amount of collected data with sporadic nodes in IoT systems. Federated learning has received increasing attention in distributed machine learning where only intermediate parameters are exchanged with training samples that resided at local nodes. Nonetheless, most of the existing federated learning schemes assume a homogeneous distribution of data. The assumption, however, does not apply to IoT systems because of the heterogeneity of the IoT architecture. The nonindependent and identical distribution (non-IID) property in data volume and statistical distribution of IoT nodes can impact the performance of an aggregated global model that fits all nodes. Existing federated learning solutions for non-IID data sets either have to train additional models or require extra data exchange to check the node distribution. However, due to resource constraints in IoT systems, these approaches will increase the burden on limited computation capacity and cause network overhead. To address the issue, in this article, a novel weight-similarity-based client clustering (WSCC) approach is proposed, in which clients are split into different groups based on their data set distributions. An affinity-propagation-based method with the cosine distance of the client’s weight parameters is designed to iteratively and automatically determine dynamic clusters. The proposed approach is ideal for IoT systems since there are no auxiliary models and extra data transmissions are needed. Through the theoretical convergence analysis and empirical results, we show that our proposed WSCC scheme outperforms the representative federated learning schemes under different non-IID settings, achieving up to 20% improvements in accuracy.

Sharing private parking spaces during their idle time periods has shown great potential for addressing urban traffic congestion and illegitimate parking problems in smart cities. In this article, aiming to address the online parking-space sharing issue while ensuring the privacy of customer parking destination locations, we propose a novel destination privacy-preserving online parking sharing (DPOPS) incentive scheme. In particular, the online parking-space sharing problem is formalized as a social welfare maximization problem in a two-sided market, where parking-space providers (PSPs) and customers are regarded as sellers and buyers. Then, novel threshold value-based rules are designed to determine winners, payments, and reimbursement. Finally, winners are matched by solving a mixed-integer nonlinear programming problem, aiming to minimize the distance between customer’s destination and allocated parking space. In addition, the location privacy of the customers’ destinations is protected by the Laplace mechanism. We prove that DPOPS achieves several economically effective properties and approximate differential privacy. We analyze the upper bound of the efficiency loss of our scheme. Extensive evaluation results demonstrate that our scheme can not only achieve good performance regarding social welfare, PSP satisfaction ratio, privacy preservation, and computation overhead but also leads to shorter travel distances for customers comparing to the baseline scheme.Note to Practitioners—In this article, we address the online parking-space sharing issue with considering the parking-space providers (PSPs) and customers’ individual utility while preserving the location privacy of customers’ destinations. Most of the previous works focused on designing a centralized mechanism for allocating parking spaces without considering the protection of the customers’ location privacy. In particular, we propose an online parking-space sharing scheme called DPOPS, including a novel threshold value-based winner determination rule and a parking-space allocation rule. The proposed scheme DPOPS allows the PSPs and customers submit their bids and asks according to their own willingness and is able to improve the utilization of private parking spaces during their idle time periods. Moreover, the location privacy of customers’ destinations is protected by the Laplace mechanism. The experiments demonstrate that the proposed approach outperforms the exponential-based scheme in terms of PSP satisfaction ratio and the travel distance for parking-space customer. The proposed scheme is helpful in managing the vacant parking space in a competitive market and can be readily implemented in the real-world online parking-space sharing systems.

The rapid and wide adoption of microgrids (MGs) and the increasing popularity of electric vehicles (EVs) have created a unique opportunity for the integration of these technologies. In this article, we address the issue of demand response of EVs during MG outages by leveraging Vehicle-to-Grid (V2G) technology. Particularly, we investigate an auction trading market that allows EVs with surplus energy to act as sellers, and EVs that want to be charged to act as buyers. A novel distributed double auction scheme is proposed to allow each buyer EV to submit multiple bids to seller EVs in different parking lots. Nonetheless, the locations of buyer EVs could be inferred by an adversary through analyzing the valuations, posing serious privacy and security risks. In this regard, a valuation-based attack scheme is investigated to validate the potential privacy risk. To defend against such an attack, we present a location privacy-preserving double auction scheme, in which the MicroGrid Central Controller (MGCC) acts as the auctioneer, solving the social welfare maximization problem of matching buyers to sellers, and the cloud is used to conduct calculations for the auctioneer, protecting the privacy of participants via homomorphic encryption. Theoretical analysis is conducted to validate our auction scheme in satisfying the designed economic and privacy properties (e.g., strategy-proofness and$k$-anonymity). The experimental results show that our auction scheme can not only mitigate the demand response problem in MGs, but also provides good performance with respect to social welfare, satisfaction ratio, computational and communication overhead, and privacy leakage.

Electroencephalography (EEG) is a brain imaging approach that has been widely used in neuroscience and clinical settings. The conventional EEG analyses usually require pre-defined frequency bands when characterizing neural oscillations and extracting features for classifying EEG signals. However, neural responses are naturally heterogeneous by showing variations in frequency bands of brainwaves and peak frequencies of oscillatory modes across individuals. Fail to account for such variations might result in information loss and classifiers with low accuracy but high variation across individuals. To address these issues, we present a systematic time-frequency analysis approach for analyzing scalp EEG signals. In particular, we propose a data-driven method to compute the subject-specific frequency bands for brain oscillations via Hilbert-Huang Transform, lifting the restriction of using fixed frequency bands for all subjects. Then, we propose two novel metrics to quantify the power and frequency aspects of brainwaves represented by sub-signals decomposed from the EEG signals. The effectiveness of the proposed metrics are tested on two scalp EEG datasets and compared with four commonly used features sets extracted from wavelet and Hilbert-Huang Transform. The validation results show that the proposed metrics are more discriminatory than other features leading to accuracies in the range of 94.93% to 99.84%. Besides classification, the proposed metrics show great potential in quantification of neural oscillations and serving as biomarkers in the neuroscience research.

With the increasing popularity of crowdsourcing services, high-dimensional crowdsourced data provides a wealth of knowledge. Nonetheless, unprecedented privacy threats to participants have emerged, due to complex correlations among multiple attributes and the vulnerabilities of untrusted crowdsourcing servers. Differential privacy-based paradigms have been proposed to release privacy-preserving datasets with statistical approximation. Nonetheless, most existing schemes are limited when facing highly correlated attributes, and cannot prevent privacy threats from untrusted crowdsourcing servers. To address this issue, we propose two novel solutions, namelyLoCopandDR_LoCop, which guarantee local differential privacy based on the randomized response technique while synthesizing and releasing high-dimensional crowdsourced data with high data utility. Particularly,LoCopleverages copula theory to synthesize high-dimensional crowdsourced data via univariate marginal distribution and attribute dependence. Univariate marginal distribution is estimated by the Lasso-based regression algorithm from aggregated privacy-preserving bit strings. Dependencies among attributes are modeled as multivariate Gaussian copula. Based onLoCop, the enhanced solutionDR_LoCopnot only takes advantage of C-vine copula to reflect conditional dependencies among high-dimensional attributes, but also achieves dimension reduction. Extensive experiments on real-world datasets demonstrate that our solutions substantially outperform the state-of-the-art techniques in terms of both data utility and computational overhead.

The densified deployment of heterogeneous networks coexisting with a variety of overlapping cells has emerged as a viable solution for next generation wireless networks. Despite numerous advantages, the heterogeneity and denseness also raise complicated handover management issue. Nonetheless, most existing handover methods generally depend on one or more objective attributes, and rarely consider the subjective demands of personalized users and specific applications that demand differentiated services. Through decomposing the control plane and data plane, software defined network(SDN) offers a flexible architectural paradigm to overcome these challenges. In this article, we first develop an SDN-driven handover architecture that is capable of perceiving global network status and requirements from various perspectives, including the physical layer, users, and applications. Then, a context-aware multi-criteria handover mechanism is developed in the SDN edge to provide differentiated services. Considering the numerous complicated factors, the handover decision is made based on a hierarchical fuzzy inference system to process diverse attributes and vague requirements described in natural language. Finally, we evaluate the performance of our proposed scheme through a combination of extensive simulations and real-world experiments. The results demonstrate that our solution outperforms the baseline handover schemes, more efficiently providing differentiated services with respect to throughput, bandwidth cost, and application satisfaction, and is efficient and feasible in practice.

The federated learning framework builds a deep learning model collaboratively by a group of connected devices via only sharing local parameter updates to the central parameter server. Nonetheless, the lack of transparency in the local data resource makes it prone to adversarial federated attacks, which have shown increasing ability to reduce learning performance. Existing research efforts either focus on the single-party attack with impractical perfect knowledge setting and limited stealthy ability or the random attack that has no control on attack effects. In this paper, we investigate a new multi-party adversarial attack with the imperfect knowledge of the target system. Controlled by an adversary, a number of compromised devices collaboratively launch targeted model poisoning attacks, intending to misclassify the targeted samples while maintaining stealthy under different detection strategies. Specifically, the compromised devices jointly minimize the loss function of model training in different scenarios. To overcome the update scaling problem, we develop a new boosting strategy by introducing two stealthy metrics. Via experimental results, we show that under both perfect knowledge and limited knowledge settings, the multi-party attack is capable of successfully evading detection strategies while guaranteeing the convergence. We also demonstrate that the learned model achieves the high accuracy on the targeted samples, which confirms the significant impact of the multi-party attack on federated learning systems.

The Industrial Internet of Things (IIoT) is a critically important implementation of the Internet of Things (IoT), connecting IoT devices ubiquitously in an industrial environment. Based on the interconnection of IoT devices, IIoT applications can collect and analyze sensing data, which help operators to control and manage manufacturing systems, leading to significant performance improvements and enabling automation. IIoT systems are characterized by a variety of IIoT applications, which generate different computing tasks depending on their functionalities. Some tasks are time sensitive (TS), while others are not, and more importantly, some tasks are nonpreemptive in IIoT scenarios. Thus, processing the different IIoT applications efficiently in an IIoT environment is key to achieving automation. Since computing resources are limited in IIoT, how to rapidly process TS tasks is a critical issue. Although some existing scheduling schemes can deal with the latency requirements of TS tasks, they lack consideration for nonpreemptive tasks. To address this issue, in this article we consider a typical smart warehouse system as an example and propose a generic task scheduling scheme that reserves computing resources to wait for upcoming TS tasks in such an IIoT environment. In doing so, our proposed scheme is capable of minimizing the overall waiting time for TS tasks. To evaluate the proposed scheme, we have implemented a simulation platform for a smart warehouse and conducted extensive experiments. Our experimental results demonstrate the efficacy of our scheme, which can allocate computing resources so that the processing time for the TS tasks can be reduced. Additionally, we discuss some potential research directions toward improving performance in IIoT environments with respect to resource management, machine learning, and security and privacy.

Machine learning techniques have been widely adopted to assist in data analysis in a variety of Internet of Things (IoT) systems. To enable flexible use of trained learning models, one viable solution is to leverage all categories of data from different applications to train a general model, which can be further tuned for applications through tuning process. This process incurs additional overhead at the start, but makes later revision and iteration faster and more flexible. Nonetheless, due to limited computing capabilities, IoT devices cannot handle the training process of large datasets. To address this issue, in this paper, we propose a general framework to adopt transfer learning in industrial Internet of Things (IIoT) systems. In our study, we categorize the application space of applying transfer learning to IIoT systems into four generic scenarios: centralized transfer learning with large datasets, distributed transfer learning with large datasets, centralized transfer learning with small datasets, and distributed transfer learning with small datasets. According to the characteristics of each scenario, we design workflows to apply transfer learning technique. To demonstrate the efficacy of the approach, we apply our transfer learning technique to the task of IIoT component recognition. We use the known VGG-16 model and leverage T-Less industrial datasets to evaluate the performance of our approach in different scenarios. Via performance evaluation, our experimental results confirm the efficacy of our approach, which can not only reduce training time, but also achieve higher accuracy, compared with the classical convolutional neural network (CNN) approach.

In Internet-of-Things (IoT)-driven smart-world systems, real-time crowdsourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a larger population, thus providing more reliable knowledge for our society. Particularly, multiple distributed servers in a decentralized network can realize real-time collaborative statistical estimation by disseminating statistics from their separate databases. Despite no raw data sharing, the real-time statistics could still expose the data privacy of crowdsourcing participants. For mitigating the privacy concern, while the traditional differential privacy (DP) mechanism can be simply implemented to perturb the statistics in each timestamp and independently for each dimension, this may suffer a great utility loss from the real-time and multidimensional crowdsourced data. Also, the real-time broadcasting would bring significant overheads in the whole network. To tackle the issues, we propose a novel privacy preserving and communication-efficient decentralized statistical estimation algorithm (DPCrowd), which only requires intermittently sharing the DP protected parameters with one-hop neighbors by exploiting the temporal correlations in real-time crowdsourced data. Then, with further consideration of spatial correlations, we develop an enhanced algorithm, DPCrowd+, to deal with multidimensional infinite crowd-data streams. Extensive experiments on several data sets demonstrate that our proposed schemes DPCrowd and DPCrowd+ can significantly outperform existing schemes in providing accurate and consensus estimation with rigorous privacy protection and great communication efficiency.

Industrial Internet of Things (IIoT) envisions the tight coupling of numerous critical industrial manufacturing subsystems, such as control, networking, and computing through the ubiquitous Internet of Things technologies. Nonetheless, such interconnectivity poses significant challenges to the successful management and operation of massively distributed industrial manufacturing systems. Without carefully integrated system design, the nonoptimal management and operation of highly intertwined subsystems can lead to the loss of productivity and ultimately the value of factories and plants. To address this issue, in this article, we conduct the integrated design that is capable of simultaneously configuring both control and networking subsystems in IIoT with consideration for their inherent interdependencies. We first analyze the performance of the dynamic backoff exponential (BE) in IEEE 802.15.4 carrier-sense multiple access (CSMA) and show the performance improvement of dynamic BE. We then design a model-free reinforcement learning algorithm to configure the control and networking subsystems automatically via systematic trial and error, as it is impractical to build a model for a highly intertwined complex IIoT system. Considering the time-sensitive characteristics of IIoT systems, we design priority-aware policies based on importance among networking traffic (i.e., sensing traffic and actuation traffic) to improve the convergence speed. The experimental results demonstrate that our priority-aware reinforcement-learning-based integrated design can successfully reconfigure the complex and highly intertwined IIoT system at runtime with a minimal convergence time. Besides, our approach reduces convergence time by 37.5%, and energy consumption by 9.2%, compared to the standard reinforcement learning approach.

Verifiable pattern matching enables users to obtain authenticated query results over outsourced data on an untrusted remote server. It is a fundamental problem in many security-critical big data applications, including big database search, human genome data search, text search, etc., especially when these applications are outsourced to third-party clouds. However, the state-of-the-art schemes do not yet support efficient data updates. In this work, we propose the first dynamic verifiable pattern matching scheme to support efficient data updates. The proposed scheme is built on two ideas: one is to embed unique randomness to decouple the character and its index in the outsourced data, enabling efficient data updates; the other is to reduce the verifiable pattern matching problem to a discrete set membership testing problem, which relies on the decoupling introduced in the first idea. Based on these two ideas, the proposed scheme first employs the suffix array index structure to search pattern matching queries. The scheme then authenticates the outsourced text using a newly designed authenticated data structure based on the RSA accumulator, which guarantees the verifiability of pattern matching query results. Data update is naturally supported using the RSA accumulator working on discrete sets. Based on the proposed design, we have prototyped a proof-of-concept for the proposed scheme and have conducted an extensive experimental evaluation. In addition to supporting efficient data update, our experimental results show that the proposed scheme incurs reduced verification cost in comparison with the baseline state-of-the-art scheme.

The massive generation, extensive sharing, and deep exploitation of data in the big data era have raised unprecedented privacy threats. To address privacy concerns, various privacy paradigms have been proposed to achieve a good tradeoff between privacy and data utility. Particularly, differential privacy has been well accepted as one of the de facto standard for privacy preservation, and numerous schemes guaranteeing differential privacy have been proposed. Nonetheless, most of the existing works claiming a superior utility-privacy tradeoff only present specific methods, with distinct perspectives, and a complete comparative analysis and evaluation study has not been fully investigated. To this end, in this paper we review and investigate existing schemes on providing differential privacy from a broad and encompassing perspective to provide a comprehensive survey with respect to both the privacy guarantee and the effectiveness and efficiency in utility improvement. We categorize the existing schemes into distribution optimization, sensitivity calibration, transformation, decomposition, and correlations exploitation, based on their mechanisms in improving data utility. We also conduct some analysis and comparison of their various concepts and principles, focusing on improvements to data utility. Finally, we outline some challenges and provide future research directions.

Internet of Things (IoT) communications include an exceptional number of Machine-to-Machine (M2M) devices to enable automation in smart-world systems. Given the explosion in number of distributed computing devices, traffic modeling and forecasting (TMF) schemes, which imitate and predict device traffic dynamics in M2M communications, become critical to providing useful guidance for constrained network resource planning and scheduling. In this paper, we explore the vulnerability of TMF, particularly focusing on data integrity attacks. Specifically, we consider a generic attack with adversaries impersonating M2M networks to send fake triggering messages to detached M2M devices. Thus, the number of attached M2M devices can be manipulated. We additionally conduct threat modeling and investigate attack impacts on TMF. Our experimental results show that data integrity attacks are effective in disrupting TMF to reduce goodness-of-fit and prediction accuracy.

As a typical application of the Internet of Things (IoT), the Industrial IoT (IIoT) connects all the related IoT sensing and actuating devices ubiquitously so that the monitoring and control of numerous industrial systems can be realized. Deep learning, as one viable way to carry out big-data-driven modeling and analysis, could be integrated in IIoT systems to aid the automation and intelligence of IIoT systems. As deep learning requires large computation power, it is commonly deployed in cloud servers. Thus, the data collected by IoT devices must be transmitted to the cloud for training process, contributing to network congestion and affecting the IoT network performance as well as the supported applications. To address this issue, in this article, we leverage the fog/edge computing paradigm and propose an edge computing-based deep learning model, which utilizes edge computing to migrate the deep learning process from cloud servers to edge nodes, reducing data transmission demands in the IIoT network and mitigating network congestion. Since edge nodes have limited computation ability compared to servers, we design a mechanism to optimize the deep learning model so that its requirements for computational power can be reduced. To evaluate our proposed solution, we design a testbed implemented in the Google cloud and deploy the proposed convolutional neural network (CNN) model, utilizing a real-world IIoT data set to evaluate our approach.1Our experimental results confirm the effectiveness of our approach, which cannot only reduce the network traffic overhead for IIoT but also maintain the classification accuracy in comparison with several baseline schemes.1Certain commercial equipment, instruments, or materials are identified in this article in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

Industrial Internet-of-Things (IIoT), also known as Industry 4.0, is the integration of Internet of Things (IoT) technology into the industrial manufacturing system so that the connectivity, efficiency, and intelligence of factories and plants can be improved. From a cyber physical system (CPS) perspective, multiple systems (e.g., control, networking and computing systems) are synthesized into IIoT systems interactively to achieve the operator's design goals. The interactions among different systems is a non-negligible factor that affects the IIoT design and requirements, such as automation, especially under dynamic industrial operations. In this paper, we leverage reinforcement learning techniques to automatically configure the control and networking systems under a dynamic industrial environment. We design three new policies based on the characteristics of industrial systems so that the reinforcement learning can converge rapidly. We implement and integrate the reinforcement learning-based co-design approach on a realistic wireless cyber-physical simulator to conduct extensive experiments. Our experimental results demonstrate that our approach can effectively and quickly reconfigure the control and networking systems automatically in a dynamic industrial environment.

In this paper, to address the issue of demand response in the smart grid with island MicroGrids (MGs), we introduce an effective and secure auction market that allows electric vehicles (EVs) having surplus energy to act as sellers, and the EVs having insufficient energy in the island MGs to act as buyers. There are two primary challenges in designing an effective auction market in the smart grid. First, the auction market scheme shall be online, allowing buyers and sellers to enter the market at any time, and satisfy several critical economic properties (individual rationality, incentive compatibility, and so on.). Second, the sensitive information of participants shall be protected in the auction process. To address these challenges, we present a novel privacy-preserving online double auction scheme based on differential privacy. In our auction market, the MicroGrid Center Controller (MGCC) acts as the auctioneer, aiming at solving the social welfare maximization problem to match buyers and sellers. The principle of differential privacy is leveraged to protect the privacy of EVs' sensitive bidding information. Via theoretical analysis, we demonstrate that our designed auction scheme satisfies both economic and privacy-preserving properties, including individual rationality, incentive compatibility, weak budget balance, and ε-differential privacy. We conduct an extensive performance evaluation to measure the effectiveness of our proposed scheme. Our experimental results show that the proposed auction scheme can not only ensure the privacy of participants but also effectively facilitates demand response in the smart grid, with respect to social welfare, satisfaction ratio, social efficiency, and computational overhead.

The increasing volume of network-connected devices comprising Internet of Things and the variety of heterogeneous network architectures across these devices pose significant challenges to effective deployment and routing. In this article, we consider the adoption of probabilistic data structures to develop a novel Bloom Filter-based dual-layer inter-domain routing scheme. Our designed scheme implements internal and external routing layers in network gateways constructed upon the counting bloom filter and the original bloom filter. We first compare several representative structures in both theory and experimentation. We then propose our novel Bloom Filter-based dual-layer inter-domain routing scheme. In the design of the routing scheme, we consider issues related to the overall space cost and routing loop prevention, as well as present corresponding solutions. We also detail the principal structures and algorithms. Further, we conduct a theoretical analysis of the space efficiency of our proposed scheme compared to traditional routing with respect to the size of data packets and the size of routing tables, as well as in routing loop avoidance. Finally, via extensive performance evaluation, our experimental results demonstrate the effectiveness and efficiency of our proposed scheme.

Long-term Evolution (LTE)/LTE-Advanced (LTE-A) can be a viable network infrastructure for Internet-of-Things (IoT) as it provides broadband wireless connections and wide-area coverage. However, effectively allocating limited spectrum resources remains a challenging problem due to the massive number of mobile and IoT smart devices competing for limited network resources. In this paper, we assess the performance of transmitting IoT traffic over LTE/LTE-A and discuss strategies for supporting distinct IoT-based systems. We design six cases by considering two data load patterns (i.e., small data load and large data load) from IoT devices and three transmission environments (i.e., free space, suburban, and urban). Through extensive simulation, we evaluate network performance with respect to bandwidth efficiency, throughput, packet loss ratio, and delay. Our experimental results confirm that a narrower bandwidth achieves higher bandwidth efficiency in all cases, but a narrow bandwidth could not maintain acceptable network performance when large data loads need to be transmitted over the network.

The electrocardiogram (ECG) signal, as one of the most important vital signs, can provide indications of many heart-related diseases. Nonetheless, in the case of telehealth context, the automated analysis and accurate detection of ECG signals remain unsolved issues, because the poor data quality collected by the wearable devices and unprofessional users further increases the complexity of hand-crafted feature extraction, ultimately affecting the efficiency of feature extraction and the detection accuracy. To address this issue and improve the detection accuracy, in this paper we present a novel detection scheme with the raw ECG signal in wearable telehealth system. Our system benefits from the concept of big data, sensing and pervasive computing and the emerging deep learning technology. In particular, a Deep Heartbeat Classification (DHC) scheme is proposed to analyze the ECG signal for arrhythmia detection. Distinct from existing solutions, the detection model in DHC can be trained directly on the raw ECG signal without hand-crafted feature extraction. A cloud-based prototypical system is also designed and implemented with the functions of data acquisition, wireless transmission, back-end data management, and ECG detection. The experimental results demonstrate that our prototypical system is feasible and effective in real-world practice, and extensive experimentation based on the MIT-BIH database demonstrates that the proposed DHC scheme outperforms baseline schemes.

Deep learning, as an increasingly powerful and popular data analysis tool, has the potential to improve smart grid operation. One critical issue is that the accuracy of deep learning relies heavily on the integrity of the training dataset, and the data collection process is time-consuming and complex, resulting in that the applying deep learning may not satisfy the needs of time-sensitive applications. Moreover, in the smart grid, predictions must be timely, and cannot wait for the initial dataset to be completely collected by the sensors. Also, the traditional centralized data analytics structure requires the entire dataset to be uploaded to the cloud datacenter for analysis, which incurs significant network resource and increases network congestion. To address these problems, in this paper we consider the allocation of deep learning at the network edge and directly in the Internet of Things (IoT) devices and design an online learning approach to enable small data subset training and continuous model updating to ensure accuracy requirements in time-sensitive environments. In our online learning approach, we implement the Just Another Network model, an optimized Long-Short Term Memory neural network model, to reduce the computation overhead for the deep learning training process. We evaluate our approach using real-world smart grid dataset. Our experimental results show that our online learning approach significantly reduces the training time while satisfying the accuracy requirements.

With an unparalleled number of Machine-to-Machine (M2M) devices being deployed to support a variety of smart-world systems powered by Internet of Things (IoT) technologies, the heterogeneity, uncertainty, and complexity of M2M communications have increased enormously. Thus, how to conduct network resource planning (NRP) has become a challenging issue. In this paper, we propose a novel time series framework (TSF) to model and forecast timescale network traffic dynamics in M2M communications that is capable of providing useful guidance for effective NRP. Specifically, our TSF utilizes the statistical techniques INGARCH(p,q) (integer valued generalized autoregressive conditional heteroskedasticity) and βARMA(p,q) (beta autoregressive moving average) to accurately capture both the internal and external impact factors of the asynchronous and synchronous M2M traffic dynamics over a large time scale, and produces forecasts for multiple upcoming time points by leveraging conditional maximum-likelihood estimators (CMLE). Through a combination of theoretical analysis and extensive simulation, we have validated the modeling and forecasting efficacy of TSF. Our experimental results demonstrate that TSF achieves superior performance with respect to goodness-of-fit and prediction accuracy.

In this paper, we address the issue of demand response (DR) in microgrids via vehicle-to-vehicle technology in the smart grid with consideration for location privacy protection supported by Internet of Vehicles. To enable effective DR, the online double auction is a viable approach to support energy trading between electric vehicles (EVs) that have surplus or insufficient energy, while the utility of each participant can be considered. Nonetheless, there are three primary challenges in designing such an online double auction approach. First, as EVs are allowed to enter the market at any time, the auctioneer should make the best decision without further information about bids and asks. Second, as EVs are allowed to enter the market in different places, the auctioneer should perform routing optimization for EV charging after determining the winner. Third, there is a risk of leakage in the location of EVs that needs to be protected. To tackle these issues, we present a new truthful online double auction scheme, which features multiunit energy trading among EVs, routing optimization for EV charging, and location privacy protection. We conduct a theoretical analysis and demonstrate that our online double auction scheme is capable of achieving several important economic properties as well as the privacy guarantee (i.e., k-anonymity). Our experimental results show that the proposed scheme can achieve good performance with respect to social welfare, satisfaction ratio, total profit of EV owners, peak load shifting, state of charge, driving distance satisfaction, and computing time, and can further ensure location privacy protection.

In this paper, we address the issue of the energy trading in the scenario of electric vehicles (EVs) charging in the smart grid. The EVs energy trading problems have attracted growing attention with the popularity of EVs. As the traditional first-reserve-first-serve scheme in the energy trading market impairs the benefits of both buyers and seller, we consider an auction scheme, called progressive second price (PSP), which has been proved to be an efficient way to conduct resource allocation in the trading market. Compared with other auction schemes, the PSP scheme can achieve both incentive compatibility and Nash equilibrium, which are important properties for the market. Nonetheless, the PSP auction scheme is not designed for online auction and it cannot guarantee that the seller can provide an enough number of charging piles to satisfy the demand of winners. To tackle these issues, in this paper we propose a novel online continuous PSP-based auction scheme, which is capable of not only achieving the property of online energy trading but also guaranteeing that the number of winners is limited to be no more than the number of charging piles. Further, we prove that our auction scheme achieves incentive compatibility and Nash equilibrium. The extensive experimental results demonstrate that our auction scheme achieves good performance with respect to social welfare, the seller satisfaction ratio, the buyer satisfaction ratio, as well as computation overhead.

In this paper, we investigate the threat of drones equipped with recording devices, which capture videos of individuals typing on their mobile devices and extract the touch input such as passcodes from the videos. Deploying this kind of attack from the air is significantly challenging because of camera vibration and movement caused by drone dynamics and the wind. Our algorithms can estimate the motion trajectory of the touching finger, and derive the typing pattern and then touch inputs. Our experiments show that we can achieve a high success rate against both tablets and smartphones with a DJI Phantom drone from a long distance. A 2.5" NEUTRON mini drone flies outside a window and also achieves a high success rate against tablets behind the window. To the best of our knowledge, we are the first to systematically study drones revealing user inputs on mobile devices and use the finger motion trajectory alone to recover passcodes typed on mobile devices.

With the explosive growth of mobile devices and subsequent traffic volume, densified deployment of Heterogeneous Network (HetNet) coexisting with a variety of cells with overlay coverage has emerged as a viable solution for future 5G networks. Despite many advantages, this new architecture also introduces numerous new network management issues, such as frequent handovers. Although a number of handover mechanisms have been proposed, these methods generally depend on one or more objective attributes from the perspective of the users and network, and do not consider the subjective demands of personalized users and specific applications that demand differentiated network services. In this paper, we first develop an software defined networking (SDN)-driven handover architecture that is capable of perceiving global network statements and requirements from all perspectives, including the physical layer, users, and applications. Then, a context-aware multi-criteria handover mechanism is developed in the SDN controller to provide differentiated services. Considering the many complicated factors, the handover decision is made based on a hierarchical fuzzy inference system to process diverse attributes and fuzzy information described in natural language. The evaluation results demonstrate that our scheme outperforms the baseline Received Signal Strength Indicator (RSSI)-based handover scheme, more efficiently providing differentiated services with respect to throughput, bandwidth cost, and application satisfaction.

Android represents an ever-increasing share of the worldwide smart device market. The platform's ubiquity and open nature make Android a prime target for malicious actors. Unfortunately, device fragmentation among manufacturers makes maintaining cyber security difficult, invoking the need for third party security software. We present a framework for detecting and countering deceptive user interface attacks on the Android platform via inspection and analysis of inter-process communication transactions in the operating system. We evaluate our proof of concept implementation on a known class of malware that exploits the Android display system, allowing a malicious application to control the screen and mimic any application launched by the user. We achieve 100% detection rate of this malicious behavior with no false alarms.

The number of Machine-to-Machine (M2M) devices has continued to grow at an accelerated rate. Without thoughtful and efficient resource management, M2M communications will be asymmetrically handicapped by service rate scarcity as more devices are continually added. To address these issues, in this paper, we propose a dynamic rate adaptation (DRA) scheme to obtain an optimized service rate distribution among a mixture of time-driven and event-driven M2M applications. DRA introduces real time monitoring of M2M traffic arrival rate, building on which service rate distribution between M2M applications can be adjusted momentarily, by using the mean value theorem of integrals (MVTI) and generalized processor sharing (GPS). We have validated the effectiveness of our proposed DRA scheme and our experimental results demonstrate that DRA can significantly improve M2M communications performance with respect to throughput and delay.

Blockchain, as a mechanism to decentralize services, security, and verifiability, offers a peer-to-peer system in which distributed nodes collaboratively affirm transaction provenance. In particular, blockchain enforces continuous storage of transaction history, secured via digital signature, and affirmed through consensus. In this study, we consider the recent surge in blockchain interest as an alternative to traditional centralized systems, and consider the emerging applications thereof. In particular, we assess the key techniques required for blockchain implementation, offering a primer to guide research practitioners. We first outline the blockchain framework in general, and then provide a detailed review of the component data and network structures. Additionally, we consider the breadth of applications to which blockchain has been applied, broadly implicating Internet of Things (IoT), Big Data, and Cloud and Edge computing paradigms, along with many other emerging applications. Finally, we assess the various challenges to blockchain implementation for widespread practical use, considering the security vulnerabilities to majority attacks, selfish mining, and privacy leakage, as well as performance limitations of blockchain platforms in terms of scalability and availability.

Unmanned Aerial Vehicles (UAVs), also known as drones, have become a new paradigm to provide emergency wireless communication infrastructure when conventional base stations are damaged or unavailable. In this paper, we propose new schemes to enable the 3D deployment of drones, which can provide network coverage and connectivity services for users located in uneven terrain. We formalize two models, including optimal coverage model and optimal connectivity model, which belong to NP-hard. To be specific, we first consider both the quality of service (QoS) requirements of users and the capacity of drones. We then formalize the problem and design a heuristic scheme, called Particle Swarm Optimization (PSO) algorithm to achieve a cost-effective solution. We also address the optimal connectivity problem in a scenario, in which a number of isolated local networks have been established by users through ad hoc communication and/or device-to-device (D2D) communication. We further develop the cost-effective heuristic algorithm to effectively minimize the total number of required drones. Via extensive performance evaluation, our experimental results demonstrate that the proposed schemes can achieve the effective deployment of drones for users in uneven terrain with respect to the number of required drones.

With the development of online taxi-hailing systems (DiDi, Uber Lyft, etc.), how to effectively allocate taxis has attracted great attention in the recent past. Meanwhile, with the rapid increase of taxi-related crimes, the privacy of passengers' sensitive information such as location remains a critical concern. In this paper, we present a novel incentive-based scheme, which provides the differential privacy guarantee for passengers' locations in taxi-hailing systems. To be specific, to allocate limited taxis to passengers, we first present the Vickrey-Clarke-Groves (VCG)-based online auction mechanism for determining the winning passengers. Then, to match the taxis and winning passengers as well as to protect the location privacy of passengers, we present the new allocating rule based on the exponential differential privacy-based mechanism. Further, we prove that the proposed incentive-based scheme satisfies both economic properties and 2-ε differential privacy guarantee. Finally, we evaluate the performance of our proposed scheme. The experimental data confirms that our proposed scheme not only achieves better performance than the two baseline schemes with respect to social welfare and satisfaction ratio, but also is capable of protecting the location privacy of passengers with low privacy disclosure.

In this paper, we address the issue of Android malware detection by implementing a deep learning environment and fine-tune parameters to determine optimal settings for the classification of Android malware from extracted permission data. By determining the optimal settings, we demonstrate the potential performance of a deep learning environment for Android malware detection. Specifically, we conduct an extensive study of various hyper-parameters to determine optimal configurations, and then carry out a performance evaluation on those configurations to compare and maximize detection accuracy in our target networks. Our results achieve approximately 95 % detection accuracy, with an approximate F1 score of 93 %.

In this paper, we introduce an Software Defined Networking (SDN)-based approach to support the network operations of heterogeneous hierarchical multi-domain MANETs. Our approach can seamlessly interconnect heterogeneous MANETs and reduce routing decision loads of gateway routers by decentralizing the SDN Controllers in the mid-tier level of the network between the Network Operation Center (NOC). To assess the feasibility of our proposed approach, we setup an emulation environment using the Common Open Research Emulator (CORE), and implement our designed system via the Constrained Application Protocol (CoAP), additionally integrating OLSRv2, OSPF - MDR, Babel, and others. Designing a set of test scenarios, we then carry out an extensive performance evaluation of our approach. Our experimental results show significant network performance improvements at the gateway routers with respect to overhead, packet loss ratio, and Round-Trip Time (RTT).

Communication networking leverages emerging network technologies such as topology management schemes to satisfy the demand of exponentially increasing devices and associated network traffic. Particularly, without efficient topology management, Machine-to-Machine (M2M) communications will likely asymmetrically congest gateways and eNodeBs in 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) and Long-Term Evolution Advanced (LTE-A) networks, especially when M2M devices are massively deployed to support diverse applications. To address this issue, in this paper, we propose a 3D Topology Optimization (3D-TO) scheme to obtain the optimal placement of gateways and eNodeBs for M2M communications. By taking advantage of the fact that most M2M devices rarely move, 3D-TO can specify optimal gateway positions for each M2M application, which consists of multiple M2M devices. This is achieved through global optimization, based on the distances between gateways and M2M devices. Utilizing the optimization process, 3D-TO likewise determines optimal eNodeB positions for each M2M application, based on the distances between eNodeBs and optimal M2M gateways. Our experimental results demonstrate the effectiveness of our proposed 3D-TO scheme towards M2M communications, with regard to throughput, delay, path loss, and packet loss ratio.

The development of the Internet of Things (IoT), cyber physical systems, and ubiquitous mobile terminals enable video content to be shared and consumed in new and innovative ways. Nonetheless, the stochastic and unpredictable nature of heterogeneous wireless networks with mobile clients presents a significant challenge to the increasing demand of quality of experience (QoE). In this paper, we study the problem of maximizing the user's QoE of viewing streaming video via automatic bitrate adaptation in heterogeneous wireless networks. To be specific, a stochastic optimization problem is first formulated by considering fundamental uncertainties of heterogeneous wireless networks (i.e., the stochastic throughput). A dynamic bitrate adaptation scheme is then designed based on the Lyapunov optimization framework. Our proposed scheme conducts video bitrate selection based on the current queue buffer state and real-time throughput, and is capable of balancing the tradeoff between a user's QoE and buffer occupation (i.e., memory utilization). The performance of our proposed scheme is investigated through a combination of extensive analysis, simulations, and experiments in a real-world testbed. Simulation results demonstrate that our scheme outperforms the baseline with regard to QoE and bandwidth utilization. In addition, the experimental results in real-world testbed validate the scheme's efficiency and practicality in real-world system.

Cloud storage security has been gaining research interest in recent years. Although considerable work has been conducted on verifying the integrity of the outsourced data in the cloud, how to efficiently verify the file search results returned from the cloud is still a challenge to be resolved. Towards this direction, we tackle the verifiable file search problem in this paper. We formulate and solve this problem by proposing two protocols. The first protocol enables verifying the correctness of the file search result when all users have the same security privilege in accessing the outsourced data. The second protocol, which builds on the first protocol, further enables user differentiation, i.e., different users can only access files that fit their security privileges. In our protocols, we employ two key strategies in enabling file search verifiability. One is to separate all possible filenames into two finite sets and the other is to embed some secret information in the outsourced data. Further, we leverage the key chaining and recursion mechanisms to enable user differentiation. We have conducted experiments to validate the effectiveness of our proposed protocols. Our results show that both protocols are efficient in terms of computation, storage, and communication cost.

In this paper, we present theory and a design of the online double auction for the trading of energy within a smart grid with microgrids (MGs). The online double auction has the potential to enable the allocation of surplus electricity to the MGs that need electricity with the highest gain in the real-time market. Nonetheless, two critical issues remain challenging when designing an effective online double auction scheme in such a system. First, as the agents are allowed to arrive and depart at any time, the auctioneer needs to make decisions without the information of further bids and asks. Second, the economic properties of strategy-proof, individual rational, and (weak) budget balance should be satisfied. To address these issues and enable multiunit electricity trading among local MGs, in this paper, we propose a strategy-proof online double auction (SODA) scheme, in which the surplus and insufficient MGs in the system are treated as sellers and buyers, respectively, and the MG center controller is capable of maximizing the social welfare of MGs by appropriately matching buyers and sellers. Via theoretical analysis, we prove that SODA can achieve the properties of individual rationality, (weak) budget balance, strategy-proofness, and computational efficiency. Experiments also show that SODA is capable of reducing the energy purchasing cost of the MGs and shifting the peak-load, while achieving great performance with respect to social welfare, seller/buyer satisfaction ratio, social efficiency, and computation overhead.

Lloret, J.; Ahmed, SH.; Rawat, DB.; Ejaz, W.; Yu, W. (2018). Editorial on Wireless Networking Technologies for Smart Cities. Wireless Communications and Mobile Computing (Online). 2018. doi:10.1155/2018/1865908

While smart city concept holds great promise of boosting living standards through effective management and utilization of scarce resources in cities, the unavailability of realworld datasets and test environments to evaluate designed models and techniques have slowed research progress. In this paper, we review existing research endeavors and develop a tool for extracting real-time smart city related data. We also conduct some simulations and evaluations in smart energy, which will be an important application in smart cities.

Transportation-based Cyber-Physical Systems (TCPS), also known as Intelligent Transportation Systems (ITS), have been introduced to increase traffic efficiency and safety. To reduce traffic congestion and traveling time, a number of real-time route guidance schemes have been developed to assist travelers in determining the optimal route for their transit. In this paper, we address the vulnerability issue of the route guiding process and study data integrity attacks against route guidance schemes. To be specific, we consider a generic attack, in which the adversary may compromise vehicles via wireless communication networks and then manipulate the real-time traffic information generated or forwarded by these vehicles, and finally broadcast the forged real-time traffic information into vehicular networks. We formally model the attack and quantitatively analyze its impact on the effectiveness of route guidance schemes. Our findings show that the investigated data integrity attack can effectively disrupt route guidance, resulting in significant traffic congestion, the increase of travel time, and the imbalanced use of transportation resources.

In this paper, we investigate the data integrity attack against Optimal Power Flow (OPF) with the least effort from the adversary's perspective. The investigated attack can first select the minimum number of target nodes to compromise by analyzing the difference between the capacity of transmission line and the real transmission power, and then search for a critical attack vector (with a goal to minimize the amount of information to manipulate) as an optimal attack strategy. To defend against such an attack, we develop the defensive scheme by protecting the critical nodes. Based on various IEEE standard systems, we show the effectiveness of our investigated attack scheme and the corresponding defense schemes.

Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which explores the tremendous data collected by mobile smart devices with prominent spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing Systems, temporally recruited mobile users can provide agile, fine-grained, and economical sensing labors, however their self-interest cannot guarantee the quality of the sensing data, even when there is a fair return. Therefore, a mechanism is required for the system server to recruit well-behaving users for credible sensing, and to stimulate and reward more contributive users based on sensing truth discovery to further increase credible reporting. In this paper, we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile Crowdsensing Systems, which achieves credibility-driven user recruitment and payback maximization for honest users with quality data. Via theoretical analysis, we demonstrate the correctness of our design. The performance of our scheme is evaluated based on extensive real-world trace-driven simulations. Our evaluation results show that our scheme is proven to be effective in terms of both guaranteeing sensing accuracy and resisting potential cheating behaviors, as demonstrated in practical scenarios, as well as those that are intentionally harsher.

Various paradigms, based on differential privacy, have been proposed to release a privacy-preserving dataset with statistical approximation. Nonetheless, most existing schemes are limited when facing highly correlated attributes, and cannot prevent privacy threats from untrusted servers. In this paper, we propose a novel Copula- based scheme to efficiently synthesize and release multi-dimensional crowdsourced data with local differential privacy. In our scheme, each participant's (or user's) data is locally transformed into bit strings based on a randomized response technique, which guarantees a participant's privacy on the participant (user) side. Then, Copula theory is leveraged to synthesize multi-dimensional crowdsourced data based on univariate marginal distribution and attribute dependence. Univariate marginal distribution is estimated by the Lasso-based regression algorithm from the aggregated privacy- preserving bit strings. Dependencies among attributes are modeled as multivariate Gaussian Copula, of which parameter is estimated by Pearson correlation coefficients. We conduct experiments to validate the effectiveness of our scheme. Our experimental results demonstrate that our scheme is effective for the release of multi-dimensional data with local differential privacy guaranteed to distributed participants.

In this paper, we introduce a computer vision-based attack using stereo cameras against authentication approaches for touch-enabled devices. In the attack, an attacker uses a stereo camera (such as one on the HTC Evo 3D smartphone) and takes a video of a victim entering passwords on the touch screen of the victim's mobile device. We focus on challenging scenarios where the victim holds the device up and the attacker cannot see the victim's fingertip or the device screen. Since the stereo camera provides depth and distance information of objects in video frames, we can build a 3D scene to analyze the victim's hand movement and automatically recover the victim's passcode. The 3D vision attack is stealthy in daily settings like a classroom or a coffee shop since the attacker does not need to take a suspicious angle and see the touch screen of the victim. Without loss of generality, we use graphical passwords as an example and perform extensive experiments to demonstrate the effectiveness of the attack. The success rate of the 3D vision attack reaches 90% when the camera is across a table from a victim in a typical gathering scene.

The efficiency of inter-domain routing in large-scale mobile network environments is a critical issue, as traditional prefixed addresses may fail due to the dynamic topologies of mobile networks, where devices would be required to maintain massive routing tables. To address this issue, in this paper we leverage the principles of probabilistic data structures in the inter-domain routing scheme, and propose a novel Bloom Filter-based dual-layer inter-domain routing scheme. In particular, we first compare several representative structures and develop a strategy to integrate bloom filters. We then propose our novel Bloom Filter-based dual-layer inter-domain routing scheme. In the design of the routing scheme, we address issues related to the overall space cost and routing loop prevention, and present the corresponding solutions. We also present detailed descriptions of the structures and algorithms in our routing scheme. Finally, we conduct a performance evaluation to validate the effectiveness of our proposed scheme. Our experimental results demonstrate the effectiveness and efficiency of our proposed scheme.

In this paper, we address the issue of data trading in big data markets. Data trading problems have attracted increased attention recently, as the economic benefits and potential of big data trading are substantial and varied. However, how to effectively trade data between the data owners (sellers) and data collectors/users (buyers) is far from settled, and requires careful design. Auction mechanisms have been applied across many fields, and have significant potential to facilitate data transactions in a fair, truthful, and secure way. Nonetheless, a truthful auction must ensure the property of incentive compatibility, meaning that the bidders can obtain highest utility if and only if they submit their bids and asks truthfully. Furthermore, a truthful and fair auction should also protect the optimal auction results from being manipulated by false-name bidding attacks, where users (participants) utilize multiple identities or accounts to influence the auction results. To tackle these issues, we propose a Multi-round False-name Proof Auction (MFPA) scheme, which enables data trading among data owners (sellers) and data collectors (buyers). We prove that our MFPA scheme achieves the properties of incentive compatibility, false-name bidding proofness, and computational efficiency. The experimental results demonstrate that MFPA achieves good performance in terms of social surplus, satisfaction ratio, and computation overhead.

In this paper, we address the problem of demand response of electrical vehicles (EVs) during microgrid outages in the smart grid through the application of Vehicle-to-Grid (V2G) technology. Particularly, we present a novel privacy-preserving double auction scheme. In our auction market, the MicroGrid Center Controller (MGCC) acts as the auctioneer, solving the social welfare maximization problem of matching buyers to sellers, and the cloud is used as a broker between bidders and the auctioneer, protecting privacy through homomorphic encryption. Theoretical analysis is conducted to validate our auction scheme in satisfying the intended economic and privacy properties (e.g., strategy-proofness and k-anonymity). We also evaluate the performance of the proposed scheme to confirm its practical effectiveness.

In this paper, we propose a Relay Nodes Position and Selection approach to obtain an optimal set of relay nodes for mobile nodes, which has a large amount of streaming data to be delivered in real time. Based on the selected set of relay nodes, data between mobile nodes and the core network can be transmitted via multiple paths. In our approach, we first determine the number of relay nodes, above which the performance of network cannot be further improved. We then propose a Centerof-Gravity (CoG) mechanism to properly position all the available relay nodes based on the density of mobile nodes. Finally, an optimal subset of relay nodes are selected for each mobile node to minimize the overall distance between mobile nodes and relay nodes in the network. We implement our designed approach in MATLAB and conduct an intense emulation in Common Open Research Emulator (CORE) based on the results generated in MATLAB. The experimental data confirms that our approach can improve the network performance with respect to network capacity, delay, and energy consumption.

Microgrids (MGs) have attracted growing attention due to self-sufficiency and self-healing properties. Nonetheless, the intermittent nature and uncertainty of distributed energy resources and load demands remain challenging issues in balancing demands and managing energy resources in MGs. Existing research efforts mainly focus on developing techniques to enable interactions between local MGs and the utility grid, which leads to high line power losses and operation costs. In this paper, we present the Sto2Auc framework to address the issue of stochastic optimal bidding problem for a system with MGs. First, the optimal bidding problem is formulated as a two-stage stochastic programming process, which aims to minimize the system operation cost and obtain optimal energy capacity of MGs by the MG center controller (MGCC). Uncertainties arise from both energy supply and demand, which are considered in the stochastic model, and random parameters representing those uncertainties are captured by using the Monte Carlo method. Second, to enable optimal electricity trading between the insufficient and surplus MGs, we propose a distributed double auction (DDA)-based scheme, which is proven to converge to the optimal social welfare of the system with MGs, and achieves the economical properties of being strategy-proof, individually rational, and (weak) budget balanced. Extensive experiments on an MG system composed of IEEE-33 buses demonstrate the effectiveness of proposed scheme. The experimental results show that Sto2Auc framework is capable of reducing the operational cost of MG systems, while the implemented DDA scheme achieves good performance with respect to social welfare, demand insufficiency, and MGCC profit.

The Internet of Things (IoT) has emerged as the key networking paradigm for supporting the connectivity of massively distributed objects and numerous simultaneous applications. The constrained application protocol (CoAP) is designed to meet the requirements for IoT data transmission among constrained nodes. The lean design of CoAP enables it to additionally meet the needs of the data transmission in dynamic network environments. In this paper, we conduct an emulation-based quantitative performance assessment of CoAP in comparison with HTTP, assessing data transmission based on key characteristics of dynamic network environments and the designed scenarios. We also designed scenarios and evaluate the performance of investigated protocols using real-world IoT datasets. Our experimental results demonstrate that CoAP performs better than HTTP for data transmission in the dynamic network environments with respect to delivery rate, delay, and overhead. In addition, we analyze the impact of features in dynamic network environments on the performance of data transmission protocols with respect to success rate, delay and overhead, as well as discuss some further extensions for future research.

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user's experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

Internet of Things (IoT) provides a generic infrastructure for different applications to integrate information communication techniques with physical components to achieve automatic data collection, transmission, exchange, and computation. The smart grid, as one of typical applications supported by IoT, denoted as a re-engineering and a modernization of the traditional power grid, aims to provide reliable, secure, and efficient energy transmission and distribution to consumers. How to effectively integrate distributed (renewable) energy resources and storage devices to satisfy the energy service requirements of users, while minimizing the power generation and transmission cost, remains a highly pressing challenge in the smart grid. To address this challenge and assess the effectiveness of integrating distributed energy resources and storage devices, in this paper we develop a theoretical framework to model and analyze three types of power grid systems: the power grid with only bulk energy generators, the power grid with distributed energy resources, and the power grid with both distributed energy resources and storage devices. Based on the metrics of the power cumulative cost and the service reliability to users, we formally model and analyze the impact of integrating distributed energy resources and storage devices in the power grid. We also use the concept of network calculus, which has been traditionally used for carrying out traffic engineering in computer networks, to derive the bounds of both power supply and user demand to achieve a high service reliability to users. Through an extensive performance evaluation, our data shows that integrating distributed energy resources conjointly with energy storage devices can reduce generation costs, smooth the curve of bulk power generation over time, reduce bulk power generation and power distribution losses, and provide a sustainable service reliability to users in the power grid.

In this paper, we address the security issue of optimal power flow (OPF) (as a key component in the smart grid). To be specific, we investigate the data integrity attack against OPF with the least effort from the adversary's perspective, and propose effectively defense schemes to combat the data integrity attack, with respect to the number of nodes to compromise and the amount of information to manipulate. The investigated attack can first select the minimum number of target nodes to compromise by analyzing the difference between the capacity of transmission line and the real transmission power, and then search for a critical attack vector as an optimal attack strategy. To defend against such an attack, we develop the defensive schemes by not only protecting the critical nodes but also detecting the existence of attacks based on false measurement detection schemes. Based on various IEEE standard systems, we show the effectiveness of our investigated attack scheme and the corresponding defense schemes. The experimental results show that the discovered compromised nodes and critical attack vector could lead to the increase of the fuel cost from the power generation by compromising the least number of nodes and injecting the least amount of false information, in comparison with the random attack as the baseline attack strategy. In addition, our two developed defensive schemes are capable of making OPF resilient to the data integrity attack via protecting critical nodes and identifying the falsified measurements accurately in the system.

In recent years, the smart grid has been recognized as an important form of the Internet of Things application. In the smart grid, as an energy-based cyber-physical system, the advanced metering infrastructure (AMI) will be developed to monitor and control the power grid by integrating computing and networking components to ensure stable and efficient operation. The AMI is vulnerable to cyber attacks, especially data integrity attacks. There have been a number of research efforts on detecting such attacks. Nonetheless, most of existing schemes either rely on predefined thresholds or require external knowledge. This may lead to low detection accuracy when the thresholds are improperly defined, and where there is a lack of the external knowledge. To address these issues, in this paper, we propose a Gaussian-mixture model-based detection scheme to mitigate data integrity attacks. Not relying upon the predefined thresholds or external knowledge, our developed scheme operates through narrowing the range of normal data, which can be obtained through clustering the historical data and learning minimum and maximum values or distance values to each center of individual clusters. To evaluate the effectiveness of our proposed scheme, we conduct performance simulation based on the ElectricityLoadDiagrams20112014 data set, and then analyze the effectiveness of the proposed scheme with respect to detection accuracy and overhead. The results of our investigation show that our scheme could achieve a higher detection rate, and a lower error rate, in comparison to existing schemes based on the Min-Max model.

A typical cyber-physical system (CPS) refers to a system that features a tight integration of computation, networking, and physical elements for interactions between cyber and physical spaces. The Internet of Things (IoT) is considered to be the networking infrastructure of CPS. Applications of CPS cover numerous smart-world research areas that our daily life depends upon, including smart transportation, smart electrical power grid, smart cities, smart medical systems, smart manufacturing systems, and others. While major research on improving the efficiency and reliability of CPS by using advanced information and communication technologies has been conducted, the risks of cyberspace security and privacy breaches in CPS need to be seriously investigated before a massive deployment of CPS technologies can or should be realized.

State estimation plays a critical role in self-detection and control of the smart grid. Data integrity attacks (also known as false data injection attacks) have shown significant potential in undermining the state estimation of power systems, and corresponding countermeasures have drawn increased scholarly interest. Nonetheless, leveraging optimal phasor measurement unit (PMU) placement to defend against these attacks, while simultaneously ensuring the system observability, has yet to be addressed without incurring significant overhead. In this paper, we enhance the least-effort attack model, which computes the minimum number of sensors that must be compromised to manipulate a given number of states, and develop an effective greedy algorithm for optimal PMU placement to defend against data integrity attacks. Regarding the least-effort attack model, we prove the existence of smallest set of sensors to compromise and propose a feasible reduced row echelon form (RRE)-based method to efficiently compute the optimal attack vector. Based on the IEEE standard systems, we validate the efficiency of the RRE algorithm, in terms of a low computation complexity. Regarding the defense strategy, we propose an effective PMU-based greedy algorithm, which cannot only defend against data integrity attacks, but also ensure the system observability with low overhead. The experimental results obtained based on various IEEE standard systems show the effectiveness of the proposed defense scheme against data integrity attacks.

In this paper, we investigate a novel real-time pricing scheme, which considers both renewable energy resources and traditional power resources and could effectively guide the participants to achieve individual welfare maximization in the system. To be specific, we develop a Lagrangian-based approach to transform the global optimization conducted by the power company into distributed optimization problems to obtain explicit energy consumption, supply, and price decisions for individual participants. Also, we show that these distributed problems derived from the global optimization by the power company are consistent with individual welfare maximization problems for end-users and traditional power plants. We also investigate and formalize the vulnerabilities of the real-time pricing scheme by considering two types of data integrity attacks: Ex-ante attacks and Ex-post attacks, which are launched by the adversary before or after the decision-making process. We systematically analyze the welfare impacts of these attacks on the real-time pricing scheme. Through a combination of theoretical analysis and performance evaluation, our data shows that the real-time pricing scheme could effectively guide the participants to achieve welfare maximization, while cyber-attacks could significantly disrupt the results of real-time pricing decisions, imposing welfare reduction on the participants.

To protect users from numerous password inference attacks, we invent a novel context aware privacy enhancing keyboard (PEK) for Android touch-based devices. Usually PEK would show a QWERTY keyboard when users input text like an email or a message. Nevertheless, whenever users enter a password in the input box on his or her touch-enabled device, a keyboard will be shown to them with the positions of the characters shuffled at random. PEK has been released on the Google Play since 2014. However, the number of installations has not lived up to our expectation. For the purpose of usable security and privacy, we designed a two-stage usability test and performed two rounds of iterative usability testing in 2016 and 2017 summer with continuous improvements of PEK. The observations from the usability testing are educational: (1) convenience plays a critical role when users select an input method; (2) people think those attacks that PEK prevents are remote from them.

Within the foreseeable future, the growing number of mobile devices, and their diversity, will challenge the current network architecture. Furthermore, users will expect greater data rates, lower latency, lower packet drop rates, etc. in future wireless networks. Ultra Dense Networks (UDN), considered to be one of the best ways to meet user expectations and support future wireless network deployment, will face multiple significant hurdles, including interference, mobility, and cost. In this paper, we review existing research efforts toward addressing those challenges and present future avenues for research. We first develop a taxonomy to review and describe existing research efforts. Next, we focus on inter-cell interference, handover performance, and energy efficiency as the key techniques to addressing the most pressing challenges. Finally, we present several future research directions, including emergent Internet-of-Things (IoT) applications, security and privacy, modeling and realistic simulations, and relevant techniques.

In the smart grid, the Advanced Metering Infrastructure (AMI) will be deployed to monitor and control the power grid by integrating both computing and networking components to achieve stable and efficient operation. The AMI is vulnerable to cyber attacks, especially in the form of data integrity attacks. A number of research efforts have been devoted to detecting such attacks. Nonetheless, the majority of existing schemes either rely on a pre-defined threshold, or require external knowledge. This leaves open the possibility for low detection accuracy when the threshold is improperly defined, and where there is a lack of the requisite external knowledge. To address this issue, in this paper we propose a Gaussian-Mixture Model-based Detection (GMMD) scheme to combat data integrity attacks. Not relying upon the pre-defined threshold or external knowledge, our scheme operates by narrowing the range of normal data that can be obtained by clustering the historical data and learning the minimum and maximum values of individual clusters. To validate the effectiveness of our scheme, we conduct performance evaluation based on the ElectricityLoadDiagrams20112014 data set, and analyze the effectiveness of the proposed scheme with respect to detection accuracy.The results of our investigation demonstrate that our scheme can achieve a higher detection rate, and lower error rate, in comparison with existing schemes based on the Min-Max model.

Various attacks may disclose sensitive information such as passwords of mobile devices. Residue-based attacks exploit oily or heat residues on the touch screen, computer vision based attacks analyze the hand movement on a keyboard, and sensor based attacks measure a device's motion difference via motion sensors as different keys are tapped. A randomized soft keyboard may defeat these attacks. However, a randomized key layout is counter-intuitive and users may be reluctant to adopt it. In this paper, we introduce a novel and intuitive input system, secure finger mouse, which uses a mobile device's camera sensing the fingertip movement, moves an on-screen cursor and performs clicks by sensing click gestures. We design a randomized mouse acceleration algorithm so that the adversary cannot infer keys clicked on the soft keyboard by observing the finger movement. The secure finger mouse can defeat attacks including residue, computer vision and motion based attacks too. We perform both theoretical analysis and real-world experiments to demonstrate the security and usability of the secure fingertip mouse.

The Ultra Dense Network (UDN), as a key enabler for future wireless networks (such as 5G), is comprised of a massive number of small cells in the network. Nonetheless, energy consumption will be non-negligible when a large number of smallcell Base Stations (BSs) are densely deployed. One practical and effective approach to reduce the energy consumption of the UDN is through dynamically controlling the power saving mode of BSs, while the challenge is to maintain network coverage and satisfy the performance requirements of User Equipment (UEs). In this paper, we formalize the problem of minimizing the energy consumption of BSs by optimally controlling the BS's power saving mode (switching between awake mode and sleep mode). We focus on optimal BS selection with the objective of energy efficiency, while the considering the constraints of the coverage of UEs, the capacity of BSs, and the data rate UEs. To validate the effectiveness of our proposed scheme, we have conducted performance evaluations with a comprehensive scenario design, consisting of UE density, distribution, and mobility, as well as BS deployment. The evaluation results demonstrate favorable energy efficiency improvement at averages of 38.82 % and 48.05 % in scenarios where UEs are uniformly distributed and non-uniformly distributed in the network. Meanwhile, network coverage and UE's Quality of Service (QoS) requirements are provided.

In this paper, we address the issue of designing an effective distributed real-time pricing scheme in the smart grid and investigating its security resilience when the data integrity attack is in place. Different from existing research efforts, in this paper we develop a distributed real-time pricing scheme, which can maximize the welfare of all participants and improve the resilience to system failures, as well as consider both renewable and traditional power resources. By leveraging the distributed approach, we leverage the gradient projection mechanism to solve the distributed real-time pricing problem in participants' smart meters to improve the resilience to system failures. We also investigate the vulnerabilities of the distributed real-time pricing scheme by considering one typical data integrity attack, which can inject false data into communication interfaces. Via a combination of both theoretical analysis and performance evaluation, we demonstrate that the proposed distributed scheme can effectively guide the participants to achieve individual welfare maximization. Our findings also show that data integrity attacks can disrupt the distributed real-time pricing, posing a damage to the welfare of participants.

To attract and encourage the best and the brightest students to pursue state, federal and tribal government careers in cybersecurity, the National Science Foundation's CyberCorps: Scholarship for Service (SFS) program was created and funded in 2000 as part of the Federal Cyber Services training and education initiative. Only institutions with a strong and established academic program in cybersecurity are eligible to participate in the CyberCorps SFS program. For the past four years, Towson University (TU) has offered CyberCorps scholarships to highly-qualified undergraduate students in computer science with a track in computer security. The poster describes the TU cybersecurity curriculum and the extracurricular activities that have been essential in bringing the CyberCorps SFS program to TU and recruiting qualified undergraduate students, as well as, challenges and lessons learned.

The unyielding trend of increasing cyber threats has made cyber security paramount in protecting personal and private intellectual property. In order to provide the most highly secured network environment, network traffic monitoring and threat detection systems must handle real-time data from varied and branching places in enterprise networks. Though numerous investigations have yielded real-time threat detection systems, in this paper we addressed the issue of handling the large volumes of network traffic data of enterprise systems, while simultaneously providing real-time monitoring and detection remain unsolved. Particularly, we introduced and evaluated a streaming-based threat detection system that can rapidly analyze highly intensive network traffic data in real-time, utilizing the streaming-based clustering algorithms to detect abnormal network activities. The developed system integrates the streaming and high-performance data analysis capabilities of Flume, Sharp, and Hadoop into a cloud-computing environment to provide network monitoring and intrusion detection. Our performance evaluation and experimental results demonstrate that the developed system can cope with a significant volume streaming data with high detection accuracy and good system performance.

Mobile device security must keep up with the increasing demand of mobile users. Smartphones are every day becoming connected to more devices and services, interacting with the growing Internet of things. Every new service, and connection, creates a new pathway for intrusion and data theft. Each intrusion can yield further opportunities for breaches of corporate and enterprise infrastructure, and significant cost. In our study, we propose a mobile security platform that combines our developed security web server, analysis module, and Android OS application, with the Google Cloud Messaging service for queued and targeted device messaging. In the cloud, the developed LAMP (Linux, Apache, MySQL, PHP) server sends, receives, and stores data from a connected device via the corresponding Android OS application. The data consists of system information for device identification, and application data to be distributed to the analysis module for malicious content to be extracted and identified. The analysis module, utilizing the Weka software, performs both static and dynamic analyses to detect Android malware, simultaneously providing rapid and intuitive security with predictive capabilities. The server additionally provides device status visualization and manual security operations.

In the smart grid, as a large-scale distributed cyber-physical system, Demand Response (DR) plays an important role in the electricity market. Various demand response schemes have been developed to improve the efficiency and economy of power utilization. Nonetheless, most existing schemes, including both market-led and system-led schemes, do not carefully take the information security into account in the DR process so that the power grid could suffer from cyber attacks (data integrity attacks, etc.). To address this issue, in this paper we proposed a resilient demand response scheme based on microgrids, which can achieve both great effectiveness of energy use and security resilience against data integrity attacks. In our scheme, the DR process considers two power distribution stages. In the intra-microgrid stage, the DR providers generate the list of possible electricity prices and schedules for power delivery. In the inter-microgrid stage, utilities select the proper electricity price and the schedule for power delivery. In this way, the damage impact of attacks on the power grid can be limited only within isolated microgrids that are compromised, while other microgrids that are not compromised can operate effectively. Our experimental results show that our scheme can not only bring better benefits to all participants, but also achieve a greater security resilience in the DR process in comparison with existing schemes.

Islanded Microgrids (IMG) have attracted much attention in the research and development of the smart grid, which is a large-scale distributed cyber-physical system. To overcome the limitations of the single IMG on energy efficiency and economic, In this paper, we first proposed a novel self-sufficient system, namely “Archipelago microgrid (MG)”, which is comprised of multi-microgrids while disconnected with the utility grid. We formalized the EV (electric vehicle) penetration problem as an optimization mixed integer nonlinear programming, which aims to minimize the emission and operation cost in the system. To enable a reasonable deployment of EV in each MGs, we developed two scheduling schemes, namely Unlimited Coordinated Scheme (UCS) and Limited Coordinated Scheme (LCS), respectively. A decentralized algorithm was also developed to solve the optimization model in LCS. A simulation study based on an modified IEEE-9 bus system with three MGs show that our proposed schemes can reduce both the environmental pollution created by CO2 emission and operation cost. Especially, with the consideration of peak load limits and resident preferences, the LCS scheme can obtain better results than the UCS scheme, leading to the reduction of the environmental pollution by 15.2% raised by CO2 emission, as well as the total cost by 10.8% in the system.

Abstract State estimation is a very critical component in smart grid, a typical energy‐based cyber‐physical system. Kalman filter has been widely used in the dynamic state estimation of power systems. Although a large number of research efforts have been made on the robustness and filtering effectiveness, little effort has been conducted on cyber attacks against Kalman filtering. To address this issue, in this paper we systematically compare three representative Kalman filtering techniques and formalize the problem of anomaly detection against false data injection attacks in Kalman filter. On the basis of our modeling results, we investigate five novel attack approaches that can bypass the anomaly detection. To defend against those attacks, we develop two countermeasures: the enhancement of Kalman filtering and the temporal‐based detection algorithm. We conduct extensive performance evaluation and our data validates our theoretical finding well. Copyright © 2013 John Wiley & Sons, Ltd.

Logitech made the following statement in 2009: “Since the displacements of a mouse would not give any useful information to a hacker, the mouse reports are not encrypted.” In this paper, we prove the exact opposite is true-i.e., it is indeed possible to leak sensitive information such as passwords through the displacements of a Bluetooth mouse. Our results can be easily extended to other wireless mice using different radio links. We begin by presenting multiple ways to sniff unencrypted Bluetooth packets containing raw mouse movement data. We then show that such data may reveal text-based passwords entered by clicking on software keyboards. We propose two attacks, the prediction attack and replay attack, which can reconstruct the on-screen cursor trajectories from sniffed mouse movement data. Two inference strategies are used to discover passwords from cursor trajectories. We conducted a holistic study over all popular operating systems and analyzed how mouse acceleration algorithms and packet losses may affect the reconstruction results. Our real-world experiments demonstrate the severity of privacy leakage from unencrypted Bluetooth mice. We also discuss countermeasures to prevent privacy leakage from wireless mice. To the best of our knowledge, our work is the first to demonstrate privacy leakage from raw mouse data.

The multistep electricity price (MEP) policy has been introduced by many countries to promote energy saving, load balancing, and fairness in electricity consumption. Nonetheless, with the development of the smart grid, how to determine the quantity of electricity and at what price in a step-like fashion has not been fully investigated in the past. To address this issue, in this paper, we introduce two types of MEP models: a one-dimensional MEP model and a two-dimensional MEP model, which can be used to formally analyze and determine the desirable quantities of electricity and pricing in multiple steps. Particularly, in the one-dimensional MEP model, the steps are scaled only by the quantity of electricity whereas in the two-dimensional MEP model, the steps are scaled by both the quantity of electricity and the time when the electricity is used. Based on the proposed MEP models, we further investigate the vulnerability of the electricity market operation and investigate false data injection attacks against electricity prices and charges to consumers. Through an extensive simulation study, our data shows that the proposed MEP models can achieve fairness in electricity consumption, balance loads between peak and non-peak times, and improve electricity resource utilization. Our data also indicates that false data injection attacks can only partially compromise prices in our MEP models, leading to a limited impact on users' charges.

In real-time monitoring systems, fine-grained measurements would pose great privacy threats to the participants as real-time measurements could disclose accurate people-centric activities. Differential privacy has been proposed to formalize and guide the design of privacy-preserving schemes. Nonetheless, due to the correlations and high fluctuations in time-series data, it is hard to achieve an effective privacy and utility tradeoff by differential privacy mechanisms. To address this issue, in this paper, we first proposed novel multi-dimensional decomposition based schemes to compress the noise and enhance the utility in differential privacy. The key idea is to decompose the measurements into multi-dimensional records and to achieve differential privacy in bounded dimensions so that the error caused by unbounded measurements can be significantly reduced. We then extended our developed scheme and developed a binary decomposition scheme for privacy-preserving time-series aggregation in real-time monitoring systems. Through a combination of extensive theoretical analysis and experiments, our data shows that our proposed schemes can effectively improve usability while achieving the same level of differential privacy than existing schemes.

In this paper, we investigate a novel real-time pricing scheme, which considers both renewable energy resources and traditional power resources, and can effectively guide the participants to achieve individual welfare maximization. Particularly, we develop a Lagrangian- based approach that transforms the global optimization conducted by the power company to distributed optimization problems. We show that these distributed problems are consistent with individual welfare maximization problems for end-users and traditional power plants. We also investigate vulnerabilities of the real-time pricing scheme by considering two types of data integrity attacks, i.e., injecting false data into demand-users and injecting false data into supply- users. Through a combination of theoretical analysis and performance evaluation, our data shows that the proposed real-time pricing scheme can effectively guide the participants to achieve welfare maximization. Our data also shows that data integrity attacks can effectively disrupt the results of real-time pricing decisions, posing welfare reduction on participants.

Traffic efficiency and safety are major hallmarks of Intelligent Transportation Systems (ITS). To accurately validate and investigate the effectiveness of traffic efficiency and safety application of ITSs, realistic studies are highly demanded [1]. In this paper, using real-world traffic and simulation data, we developed a realistic ITS test bed and a mobile application known as the Incident Warning Application (IWA) with the view of answering the following question: what is the traffic efficiency and safety benefits of Vehicle-to-Infrastructure (V2I) communications in a realistic ITS environment? Our real-world dataset consists of six weeks road traffic data of the Maryland (MD)/Washington DC and Virginia (VA) areas from August 8th, 2012 to September 27th, 2012. Our evaluation data shows that vehicles running our IWA application show improvements in almost all of the performance metrics evaluated. Specifically, our data shows that improvements in travel time (139.89%), fuel consumption (11.77%), and environmental emissions - carbon dioxide [CO2] (11.77%), etc. can be achieved through V2I communication.

Real-time monitoring systems can introduce numerous benefits to the participants in terms of performing data mining and analysis. Nonetheless, due to the correlations in time-series data, it is hard to achieve an effective privacy and utility tradeoff through a normal differential privacy mechanism. To address this issue, we propose novel multi-dimensional decomposition based schemes, which can greatly improve the utility in differential privacy. After extending the developed scheme, we then develop a binary decomposition scheme for time-series aggregation in real-time monitoring systems. Through both extensive theoretical analysis and experiments, our data shows that our proposed schemes can effectively improve usability while achieving the same level of differential privacy than existing schemes.

Despite the increasing capabilities, mobile devices still cannot satisfy the computation requirement of many applications. Intuitively, this can be solved by outsourcing tasks to external resources such as a remote server, cloud, or closely deployed cloudlet. However, all of them require extra infrastructures. In this paper, we consider a proximate-mobile-device based communication system in which all tasks and resources are under the control of a central scheduler. We propose a friendship-based task scheduling algorithm to address the contentions when resources are not sufficient. We also present two attack models including the denial-of-service (DoS) attack and the collusion attack. We evaluate the performance of the proposed algorithm along with another contribution-based task scheduling algorithm through extensive experiments.

To enhance the reliability and efficiency of energy service in the smart grid, the concept of the microgrid has been proposed. Nonetheless, how to secure the dynamic microgrid partition process is essential in the smart grid. In this paper, we address the security issue of the dynamic microgrid partition process and systematically investigate three false data injection attacks against the dynamic microgrid partition process. Particularly, we first discussed the dynamic microgrid partition problem based on a Connected Graph Constrained Knapsack Problem (CGKP) algorithm. We then developed a theoretical model and carried out simulations to investigate the impacts of these false data injection attacks on the effectiveness of the dynamic microgrid partition process. Our theoretical and simulation results show that the investigated false data injection attacks can disrupt the dynamic microgrid partition process and pose negative impacts on the balance of energy demand and supply within microgrids such as an increased number of lack-nodes and increased energy loss in microgrids.

Both opportunistic routing and inter-flow network coding are useful mechanisms for improving the performance of wireless networks. Both of them exploit the broadcast nature of the wireless medium and the spatial diversity of multi-hop wireless networks. In this paper, we aim at incorporating interflow network coding into opportunistic routing for further improving the performance of wireless mesh networks (WMNs). The main issue in designing such a scheme is candidate set selection and prioritization based on a proper metric for opportunistic routing. To this end, in this paper, we first present a new metric to determine the prioritization of the forwarders in the set of candidates and then design an Inter-flow Network Coding-based Opportunistic Routing (INCOR) scheme using the defined metric. Our proposed INCOR scheme can integrate the characteristics of inter-flow network coding and opportunistic routing effectively to make full use of the broadcast nature of the wireless medium. We carry out extensive simulations to evaluate the effectiveness of the INCOR method. Our data shows that INCOR outperforms both opportunistic routing and inter-flow network coding schemes.

The smart grid is a complex system that comprises components from both the power grid and communication networks. To understand the behavior of such a complex system, co-simulation is a viable tool to capture the interaction and the reciprocal effects between a communication network and a physical power grid. In this paper, we systematically review the existing efforts of co-simulation and design a framework to explore co-simulation scenarios. Using the demand response and energy price as examples of smart grid applications and operating the communication network under various conditions (e.g., normal operation, performance degrade, and security threats), we implement these scenarios and conduct a performance evaluation of smart grid applications by leveraging a co-simulation platform.

Wireless Mesh Networks (WMNs) have potential to provide convenient broadband wireless Internet access to mobile users. With the emergence of Software-Defined Networking (SDN) paradigm that separates control plane and data plane, WMNs can be easily deployed and managed. In addition, by exploiting the broadcast nature of the wireless medium and the spatial diversity of multi-hop wireless networks, intra-flow network coding has shown a greater benefit in comparison with traditional routing paradigms in data transmission for WMNs. In this paper, we develop a novel OpenCoding protocol, which combines the SDN technique with intra-flow network coding for WMNs. Our developed protocol can simplify the deployment and management of the network and improve network performance. In OpenCoding, a controller working on the control plane makes routing decisions for mesh routers and the hop-by-hop forwarding function is replaced by network coding functions in data plane. Through a simulation study, we show the effectiveness of the OpenCoding protocol in comparison with existing schemes. Our data shows that OpenCoding outperforms both traditional routing and intra-flow network coding schemes.

In an intelligence transportation system (ITS), to increase traffic efficiency, a number of dynamic route guidance schemes have been designed to assist drivers in determining the optimal route for their travels. In order to determine optimal routes, it is critical to effectively predict the traffic condition of roads along the guided routes based on real-time traffic information to mitigate traffic congestion and improve traffic efficiency. In this paper, we propose a Dynamic En-route Decision real-time Route guidance (DEDR) scheme to effectively mitigate road congestion caused by the sudden increase of vehicles and reduce travel time. Particularly, DEDR considers real-time traffic information generation and transmission. Based on the shared traffic information, DEDR introduces Trust Probability to predict traffic conditions and dynamically en-route determine alternative optimal routes. In addition, DEDR considers multiple metrics to comprehensively assess traffic conditions and drivers can determine optimal route with individual preference of these metrics during travel. DEDR also considers effects of external factors (e.g., Bad weather, incidents, etc.) on traffic conditions. Through a combination of extensive theoretical analysis and simulation experiments, our data shows that DEDR can greatly increase the efficiency of an ITS in terms of great time efficiency and balancing efficiency in comparison with existing schemes.

The smart grid is a new type of energy-based cyber-physical system (CPS), which enables interactions between the utility provider and customers through smart meters and advanced metering infrastructures (AMI). Nonetheless, an adversary can inject misleading energy usage information to the utility provider through compromised smart meters and disrupt the grid and electricity market operations. To address this issue, in this paper, we propose an Energy Dispatching False Data Defense (EDF2D) approach, which can effectively detect the forged interactive information between customers and the utility provider with a great accuracy and mitigate the damage raised by attacks on grid operations. Particularly, EDF2D uses the historical interactive information of normal users to determine the conditional probabilities of data anomalies. Based on these conditional probabilities, a Bayesian network designed for detecting false data can be established by EDF2D, and this network is then used to confirm the authenticity of interactive information received by the utility provider originally transmitted from customers. Through a combination of theoretical analysis and performance evaluation, our experimental data shows that EDF2D can effectively detect harmful false interactive data forged by the adversary and mitigate false data injection attacks on smart grid operations.

In this paper, we addressed the issue of a stochastic optimal bidding problem for a system with microgrids (MGs). The optimal bidding problem is formulated as a two-stage stochastic programming process, which aims to minimize the system operation cost and to expand energy interactions among local MGs that are geographically close. Uncertainties come from both energy supply and demand sides (e.g., wind, solar, and load demand) are considered in the stochastic model and random parameters to represent those uncertainties are captured by using the Monte Carlo method. To enable an optimal electricity trading between local MGs, we presented two bidding schemes: (i) Cournot equilibrium based Dynamic Backtrack Energy Trading (DBET), and (ii) double auction based Dual Decomposition Auction (DDA). Experimental results on an IEEE-33 bus based system with MGs were presented to show the effectiveness of our proposed schemes. Experimental results show that our proposed bidding schemes can reduce the operation cost of the system, while the DDA scheme achieves better performance in terms of system social welfare than the DBET scheme.

ABSTRACT The Smart Grid is a new type of power grid that will use advanced communication network technologies to support more efficient energy transmission and distribution. The grid infrastructure was designed for reliability; but security, especially against cyber threats, is also a critical need. In particular, an adversary can inject false data to disrupt system operation. In this paper, we develop a false data detection system that integrates two techniques that are tailored to the different attack types that we consider. We adoptanomaly‐based detectionto detect strong attacks that feature the injection of large amounts of spurious measurement data in a very short time. We integrate the anomaly detection mechanism with awatermarking‐based detection schemethat prevents more stealthy attacks that involve subtle manipulation of the measurement data. We conduct a theoretical analysis to derive the closed‐form formulae for the performance metrics that allow us to investigate the effectiveness of our proposed detection techniques. Our experimental data show that our integrated detection system can accurately detect both strong and stealthy attacks. Copyright © 2014 John Wiley & Sons, Ltd.

In Cyber-Physical Networked Systems (CPNS), the adversary can inject false measurements into the controller through compromised sensor nodes, which not only threaten the security of the system, but also consume network resources. To deal with this issue, a number of en-route filtering schemes have been designed for wireless sensor networks. However, these schemes either lack resilience to the number of compromised nodes or depend on the statically configured routes and node localization, which are not suitable for CPNS. In this paper, we propose a Polynomial-based Compromise-Resilient En-route Filtering scheme (PCREF), which can filter false injected data effectively and achieve a high resilience to the number of compromised nodes without relying on static routes and node localization. PCREF adopts polynomials instead of Message Authentication Codes (MACs) for endorsing measurement reports to achieve resilience to attacks. Each node stores two types of polynomials: authentication polynomial and check polynomial, derived from the primitive polynomial, and used for endorsing and verifying the measurement reports. Through extensive theoretical analysis and experiments, our data shows that PCREF achieves better filtering capacity and resilience to the large number of compromised nodes in comparison to the existing schemes.

Tor is a popular low-latency anonymous communication system. It is, however, currently abused in various ways. Tor exit routers are frequently troubled by administrative and legal complaints. To gain an insight into such abuse, we designed and implemented a novel system, TorWard, for the discovery and the systematic study of malicious traffic over Tor. The system can avoid legal and administrative complaints, and allows the investigation to be performed in a sensitive environment such as a university campus. An intrusion detection system (IDS) is used to discover and classify malicious traffic. We performed comprehensive analysis and extensive real-world experiments to validate the feasibility and the effectiveness of TorWard. Our results show that around 10% Tor traffic can trigger IDS alerts. Malicious traffic includes P2P traffic, malware traffic (e.g., botnet traffic), denial-of-service attack traffic, spam, and others. Around 200 known malwares have been identified. To mitigate the abuse of Tor, we implemented a defense system, which processes IDS alerts, tears down, and blocks suspect connections. To facilitate forensic traceback of malicious traffic, we implemented a dual-tone multi-frequency signaling-based approach to correlate botnet traffic at Tor entry routers and that at exit routers. We carried out theoretical analysis and extensive real-world experiments to validate the feasibility and the effectiveness of TorWard for discovery, blocking, and traceback of malicious traffic.

Tor is a well-known low-latency anonymous communication system that is able to bypass the Internet censorship. However, publicly announced Tor routers are being blocked by various parties. To counter the censorship blocking, Tor introduced non-public bridges as the first-hop relay into its core network. In this paper, we investigated the effectiveness of two categories of bridge-discovery approaches: 1) enumerating bridges from bridge HTTPS and email servers, and 2) inferring bridges by malicious Tor middle routers. Large-scale real-world experiments were conducted and validated our theoretic findings. We discovered 2365 Tor bridges through the two enumeration approaches and 2369 bridges by only one Tor middle router in 14 days. Our study shows that the bridge discovery based on malicious middle routers is simple, efficient, and effective to discover bridges with little overhead. We also discussed issues related to bridge discovery and mechanisms to counter the malicious bridge discovery.

Pollution attacks refer to ones where attackers modify and inject corrupted data packets into the wireless network with network coding to disrupt the decoding process. In the context of network coding, the epidemic effect of pollution attacks can degrade network throughput significantly because of the mixing nature of network coding. To address this issue, a number of malicious nodes identification schemes have been developed in the past. However, these schemes have their limitations and cannot effectively deal with pollution attacks. In this paper, we propose a novel light-weight Self-checking Pollution Attackers Identification Scheme (SPAIS), which can identify the pollution attackers effectively and efficiently. Through making full use of the broadcast nature of wireless media and insight that a well-behaved node can monitor its downstream neighboring nodes locally by cooperating with other nodes, SPAIS hierarchically organizes the network as levels such that the nodes in the same level can monitor their downstream level nodes cooperatively. Through the combination of theoretical analysis and extensive simulations, our experimental data demonstrates that SPAIS can more effectively identify pollution attackers with a lower cost in comparison with the existing representative schemes. For example, even if the quality of the network connection is not in good condition and the malicious nodes send only one corrupted packet, the pollution attackers can be identified with a high probability.

Because of ever-increasing performance and capacity gains, the popularity of LTE as a 4G technology has skyrocketed. Unfortunately, cyber adversaries may launch attacks against the LTE network. In this paper, we develop a theoretical framework to systematically explore the attack space which consists of three dimensions: communication services attacked, planes of attack, and network components under attack. Based on the developed framework, we carried out extensive simulations to evaluate the impact of some representative attacks on LTE network performance. Our developed framework and simulation models enables a foundation for advancing the understanding of threats on the LTE network and assists in developing counter-measures to secure LTE networks.

The growing popularity of Android-based smart-phones have led to the rise of Android based malware. In particular, profit-motivated malware is becoming increasingly popular in Android malware distribution. These malware typically profit by sending premium-rate SMS messages and/or make premium-rate phone calls from infected devices without user consent. In this paper, we investigate the telephony framework of the Android operating system and propose a novel process user-identification (UID) based online detection scheme. Our scheme can effectively detect premium-rate and background SMS messages as well as premium-rate phone calls initiated by malware. We implemented our detection system on a Samsung Google Nexus 4 running Android Jelly Bean and tested the effectiveness of detecting real malware from Android markets. The experimental results show that our scheme is efficient and effective in detecting background messages and premium-rate messages and phone calls. Our scheme can detect and block all the background and premium-rate SMS messages and phone calls initiated by popular malware.

Tor is a popular low-latency anonymous communication system. However, it is currently abused in various ways. Tor exit routers are frequently troubled by administrative and legal complaints. To gain an insight into such abuse, we design and implement a novel system, TorWard, for the discovery and systematic study of malicious traffic over Tor. The system can avoid legal and administrative complaints and allows the investigation to be performed in a sensitive environment such as a university campus. An IDS (Intrusion Detection System) is used to discover and classify malicious traffic. We performed comprehensive analysis and extensive real-world experiments to validate the feasibility and effectiveness of TorWard. Our data shows that around 10% Tor traffic can trigger IDS alerts. Malicious traffic includes P2P traffic, malware traffic (e.g., botnet traffic), DoS (Denial-of-Service) attack traffic, spam, and others. Around 200 known malware have been identified. To the best of our knowledge, we are the first to perform malicious traffic categorization over Tor.

It is critical for a power system to estimate its operation state based on meter measurements in the field and the configuration of power grid networks. Recent studies show that the adversary can bypass the existing bad data detection schemes, posing dangerous threats to the operation of power grid systems. Nevertheless, two critical issues remain open: 1) how can an adversary choose the meters to compromise to cause the most significant deviation of the system state estimation, and 2) how can a system operator defend against such attacks? To address these issues, we first study the problem of finding the optimal attack strategy--i.e., a data-injection attacking strategy that selects a set of meters to manipulate so as to cause the maximum damage. We formalize the problem and develop efficient algorithms to identify the optimal meter set. We implement and test our attack strategy on various IEEE standard bus systems, and demonstrate its superiority over a baseline strategy of random selections. To defend against false data-injection attacks, we propose a protection-based defense and a detection-based defense, respectively. For the protection-based defense, we identify and protect critical sensors and make the system more resilient to attacks. For the detection-based defense, we develop the spatial-based and temporal-based detection schemes to accurately identify data-injection attacks.

Cooperative communication mechanisms have been proposed as an effective way of exploiting the spatial diversity to improve the quality of wireless transmissions. To the best of our knowledge, a number of research efforts have been paid to study how to employ diversity to the network layer routing design, realizing the minimum energy expenditure in the data transmission. However, there is a lack of a systematic strategy for evaluating the existing schemes. To address this issue, we first develop a taxonomy to summarize the existing energy-efficient cooperative routing algorithms and compare their pros and cons. In particular, we focus on the relay set selection strategies, which have great impact on energy saving. To fairly compare the performance of those schemes, we conduct theoretical analysis and derive three theorems to instruct energy-efficient cooperative routing. Our extensive experiments validate our findings. Our research summarizes the state-of-art research development and lay out future directions in this area.

Mobile Ad Hoc Networks (MANETs) have been widely used in commercial and tactical domains. MANETs commonly demand a robust, diverse, energy-efficient, and resilient communication and computing infrastructure, enabling network-centric operation with minimal downtime. MANETs face security risks and energy consumption. However, conducting cyber attack monitoring and detection in a MANET becomes a challenging issue because of limited resources and its infrastructureless network environment. To address this issue, we develop both lossless and lossy aggregation techniques to reduce the energy cost in information transition and bandwidth consumption while preserving the desired detection accuracy. In particular, we develop two lossless aggregation techniques: compression-based and event-based aggregation and develop a lossy aggregation technique: feature-based aggregation. We conduct real-world experiments and simulation study to evaluate the effectiveness of our proposed data aggregation techniques in terms of the energy consumption and detection accuracy.

The concept of Multistep Electricity Price (MEP) policy has been introduced by many countries to promote energy saving, load balance and fairness in electricity consumption. However, with the development of smart grid, how to determine the electricity quantity and price scaled to multiple steps has not been fully investigated. To address this issue, in this paper we study a two-dimensional MEP model to formally analyze and determine the desirable electricity quantity and price in multiple steps. In this model, the step is scaled by both electricity quantity and the time when the electricity is used. Based on the proposed MEP model, we further investigate the vulnerability of the electricity market operation and investigate false data injection attacks against electricity price and charges to consumers. Through simulation study, our data show that the proposed MEP models can achieve the fairness in electricity consumption, balance in load between peak time and non-peak time and improvement of resource utilization. Our data also indicates that false data injection attacks can only partially compromise prices in the MEP model, leading to a limited impact on users' charges.

Because of exponential growth in smart mobile devices, malware attacks on smart mobile devices have been growing and pose serious threats to mobile device users. To address this issue, we develop a malware detection system, which uses a behavior-based detection approach to deal with the detection of a large number of unknown malware. To accurately detect malware, we examine system calls to capture the runtime behavior of software, which interacts with an operating system and adopt machine learning approaches such as Support Vector Machine (SVM) and Naive Bayes learning schemes to learn the dynamic behavior of software execution. Using real-world malware and benign samples, we conduct experiments on Android devices and evaluate the effectiveness of our developed system in terms of learning algorithms, the size of training set, the length of n-grams, and the overhead in training and detection processes. Our experimental data demonstrates the effectiveness of our proposed detection system to detect malware.

The smart grid shall not only integrate the intermittent resources (IRs) to meet the diverse demands of users and reduce the greenhouse gas emission, but also integrate Electricity Vehicles (EVs) as the energy storage facility to smooth the bulk power generation over time. In this paper, we model and analyze the impact of integrating IRs and EVs on the bulk power generation in the smart grid. In particular, we introduce the reliability ratio to quantify the power generation capacity of intermittent resources and model the process of charging and discharging of EVs as a queuing system. We extend the Security-Constrained Economic Dispatch (SCED) and include the reliability limit of IRs and the number of EVs in the power generation dispatch process and formally analyze the effect of IRs and EVs on the bulk power generation. We conduct extensive simulation and our data shows that increasing IRs can decrease the bulk generation and the curve of bulk generation over time becomes smooth as the number of EVs increases.

Internet Threat Monitoring (ITM) systems have been widely deployed to detect and characterize dangerous Internet global threats such as botnet and malware propagation. Nonetheless, the effectiveness of ITM systems largely depends on the confidentiality of their monitor locations. In this paper, we investigate localization attacks aiming to identify ITM monitor location and propose the formal model of such attacks using communication channel theory. We also develop novel techniques that significantly increases the accuracy, efficiency, and secrecy of ITM localization attacks. Specifically, we introduce (i) a frequency-based modulation technique to effectively reduce the interference from the background traffic and achieve a high attack accuracy, (ii) both time and space hopping techniques to randomize signal pattern and make the attack hard to detect by the defender, and (iii) Multiple Input and Multiple Output (MIMO) based techniques to increase the attack efficiency of identifying multiple monitors simultaneously. We derive closed formulae for the performance analysis of our proposed techniques and conduct extensive simulations. Our data validate our theoretical findings and demonstrate that the adversary can identify ITM monitors accurately, efficiently, and secretly.

The smart grid poses great concern about the exposure of consumers' privacy as the fine-grained measurements in the smart metering system can expose consumer's privacy through the disclosure of accurate load profiles of home energy usage. To address this issue, in this paper we propose novel scaling perturbation based privacy-preserving schemes that can achieve great utility for fine-grained measurements in a privacy-friendly and cost-effective manner. Our schemes adopt the measurement-based scaling perturbation to hide original measurements with low cost. Through a combination of both extensive theoretical analysis and experiments, our results show that the proposed schemes can preserve consumers' privacy through fine-grained measurements and achieve a better utility-privacy tradeoff in comparison with the existing schemes.

Network-based crime has been increasing in both extent and severity and network-based forensics encapsulates an essential part of legal surveillance. A key network forensics tool is trace back, which can be used to identify true sources of suspects. Both accuracy and secrecy are essential attributes of a successful forensic trace back. In this paper, we present a class of hopping based spread-spectrum techniques for forensic trace back, which fully use the benefits of the spread spectrum approach and preserves a greater degree of secrecy. Our proposed techniques, including Code Hopping-Direct Sequence Spread Spectrum (CHDSSS), Frequency Hopping-Direct Sequence Spread Spectrum (FH-DSSS), and Time Hopping-Spread Spectrum (TH-DSSS), operate to randomize the effects of marking traffic through both the time and frequency domains. Our simulation study validates these techniques in terms of accuracy and secrecy.

ABSTRACT Recently, the direct sequence spread spectrum (DSSS)‐based technique has been proposed to trace anonymous network flows. In this technique, homogeneous pseudo‐noise (PN) codes are used to modulate multiple bit signals that are embedded into the target flow as watermarks. This technique could be maliciously used to degrade an anonymous communication network. In this paper, we propose an effective single flow‐based scheme to detect the existence of these watermarks. Our investigation shows that, even if we have no knowledge of the applied PN code, we are still able to detect malicious DSSS watermarks via mean‐square autocorrelation (MSAC) of a single modulated flow's traffic rate time series. MSAC shows periodic peaks because of self‐similarity in the modulated traffic caused by homogeneous PN codes that are used in modulating multiple bit signals. Our scheme has low complexity and does not require any PN code synchronization. We evaluate this detection scheme's effectiveness via simulations. Our results demonstrate a high detection rate with a low false positive rate. Real‐world experiments on Tor also validate the feasibility of the detection scheme. Our scheme is more flexible and accurate than the existing multiflow‐based approach in DSSS watermark detection. We also present a theory for reconstructing the DSSS code once the DSSS code length is known and simulations validate the feasibility. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we present a study on the anonymity of Anonymizer, a well-known commercial anonymous communication system. We discovered the architecture of Anonymizer and found that the size of web packets in the Anonymizer network can be very dynamic at the client. Motivated by this finding, we investigated a class of novel packet size-based covert channel attacks against Anonymizer. The attacker between a website and the Anonymizer server can manipulate the web packet size and embed secret signal symbols into the target traffic. An accomplice at the user side can sniff the traffic and recognize the secret signal. In this way, the anonymity provided by Anonymizer is compromised. We developed intelligent and robust algorithms to cope with the packet size distortion incurred by Anonymizer and Internet. We developed techniques to make the attack harder to detect: 1) We pick up right packets of web objects to manipulate to preserve the regularity of the TCP packet size dynamics, which can be measured by the Hurst parameter; 2) We adopt the Monte Carlo sampling technique to preserve the distribution of the web packet size despite manipulation. We have implemented the attack over Anonymizer and conducted extensive analytical and experimental evaluations. It is observed that the attack is highly efficient and requires only tens of packets to compromise the anonymous web surfing via Anonymizer. The experimental results are consistent with our theoretical analysis.

Raw mouse movement data can be sniffed via off-the-shelf tools. In this demo, we show that such data, while seemingly harmless, may reveal extremely sensitive information such as passwords. Nonetheless, such a Bluetooth-mouse-sniffing attack can be challenging to perform mainly because of two reasons: (i) packet loss is common for Bluetooth traffic, and (ii) modern operating systems use complex mouse acceleration strategies, which make it extremely difficult, if not impossible, to reconstruct the precise on-screen cursor coordinates from raw mouse movements. To address those challenges, we have conducted an extensive and careful study, over multiple operating systems, on the reconstruction of mouse cursor trajectory from raw mouse data and the inference of privacy-sensitive information - e.g., user password - from the reconstructed trajectory. Our experimental data demonstrate the severity of privacy leaking from un-encrypted Bluetooth mouse. To the best of our knowledge, our work is the first to retrieve sensitive information from sniffed mouse raw data. Video links of successful replay attack for different target OS are given in Section 3.2.

Smart Grid is a new type of power grid that will provide reliable, secure, and efficient energy transmission and distribution. Cyber attacks against data readmission system threaten the security of smart grid. Hence, identifying and preventing the false data injection as early as possible becomes a critical issue. However, there is no existing solution that considers all aspects such as deployment cost and system efficiency. In this paper, we apply a light-weight watermarking technique to defend against false data injection attacks. To be specific, we add a secure watermark to real-time meter readings and transmit the watermarked data through high speed unsecured network. The utility can then correlate the watermarked data with the original watermark to detect the presence of false data injected by adversary. Our simulation results show that watermarking technique can effectively detect any false manipulation to the watermarked data at low cost.

Network coding provides a promising alternative to the traditional store-and-forward transmission paradigm. For the system using random linear network coding, the adversary could inject corrupted messages into the networks by compromising the network nodes, which is known as the pollution attack. Corrupted messages injected by the adversary, if undetected, could cause a devastating impact to the network performance. To address this issue, a number of pollution attack defense schemes for network coding have been developed in the recent years. The overhead caused by defensive techniques against pollution attacks should be low especially in wireless networks with limited resources. However, there is lack of a systematical strategy for evaluating those schemes and establishing a foundation for designing attack pollution defense schemes for network coding in wireless networks. Towards this end, we first develop the taxonomy of existing network coding authentication schemes. To fairly compare the effectiveness of those schemes, we conduct theoretical analysis and implement different schemes within the same security level. Our extensive simulation and implementation results validate our findings well. Our research summarizes the state-of-art research development and lay out future directions in this area.

In Cyber-Physical Networked Systems (CPNS), attackers could inject false measurements to the controller through compromised sensor nodes, which not only threaten the security of the system, but also consumes network resources. To deal with this issue, a number of en-route filtering schemes have been designed for wireless sensor networks. However, these schemes either lack resilience to the number of compromised nodes or depend on the statically configured routes and node localization, which are not suitable for CPNS. In this paper, we propose a Polynomial-based Compromised-Resilient En-route Filtering scheme (PCREF), which can filter false injected data effectively and achieve a high resilience to the number of compromised nodes without relying on static routes and node localization. Particularly, PCREF adopts polynomials instead of MACs (message authentication codes) for endorsing measurement reports to achieve the resilience to attacks. Each node stores two types of polynomials: authentication polynomial and check polynomial derived from the primitive polynomial, and used for endorsing and verifying the measurement reports. Via extensive theoretical analysis and simulation experiments, our data show that PCREF achieves better filtering capacity and resilience to the large number of compromised nodes in comparison to the existing schemes.

Tor is a well-known low-latency anonymous communication system that is able to bypass Internet censorship. However, publicly announced Tor routers are being blocked by various parties. To counter the censorship blocking, Tor introduced nonpublic bridges as the first-hop relay into its core network. In this paper, we analyzed the effectiveness of two categories of bridge-discovery approaches: (i) enumerating bridges from bridge https and email servers, and (ii) inferring bridges by malicious Tor middle routers. Large-scale experiments were conducted and validated our theoretic findings. We discovered 2365 Tor bridges through the two enumeration approaches and 2369 bridges by only one Tor middle router in 14 days. Our study shows that the bridge discovery based on malicious middle routers is simple, efficient and effective to discover bridges with little overhead. We also discussed the mechanisms to counter the malicious bridge discovery.

Information leakage via side channels has become a primary security threat to encrypted web traffic. Existing side channel attacks and corresponding countermeasures focus primarily on packet length, packet timing, web object size and web flow size. However, we found that encrypted web traffic can also leak information via network delay between a user and the web sites that she visits. Motivated by this observation, we investigate a novel network-delay based side-channel attack to infer web sites visited by a user. The adversary can utilize pattern recognition techniques to differentiate web sites by measuring sample mean and sample variance of the round-trip time (RTT) between a victim user and web sites. We theoretically analyzed the damage caused by such an adversary and derived closed-form formulae for detection rate, the probability that the adversary correctly recognizes a web site. To defeat this side-channel attack, we proposed several countermeasures. The basic idea is to shape traffic from different web sites so that they have similar RTT statistics. We proposed the strategies based on the k-means clustering and K-Anonymity to ensure that traffic shaping will not cause excessive delay while providing a predictable degree of anonymity. We conducted extensive experiments and our empirical results match our theory very well.

"The Marauder's Map,” a magical map in J.K. Rowling's fantasy series Harry Potter and the Prisoner of Azkaban [CHECK END OF SENTENCE], can be used as a surveillance tool to show all moving objects within the boundary of "Hogwarts School of Witchcraft and Wizardry” at a spell. In this paper, we introduce a similar forensic surveillance tool for wireless networks. Our system, the digital Marauder's map, can reveal the locations of WiFi-enabled mobile devices within the coverage area of a high-gain antenna. The digital Marauder's map is built solely with off-the-shelf wireless equipments, and features a mobile design that can be quickly deployed to a new location for instant usage without training. We present a comprehensive set of theoretical analysis and experimental results which demonstrate the coverage and localization accuracy of the digital Marauder's map.

Various low-latency anonymous communication systems such as Tor and Anonymizer have been designed to provide anonymity service for users. In order to hide the communication of users, most of the anonymity systems pack the application data into equal-sized cells (e.g., 512 B for Tor, a known real-world, circuit-based, low-latency anonymous communication network). Via extensive experiments on Tor, we found that the size of IP packets in the Tor network can be very dynamic because a cell is an application concept and the IP layer may repack cells. Based on this finding, we investigate a new cell-counting-based attack against Tor, which allows the attacker to confirm anonymous communication relationship among users very quickly. In this attack, by marginally varying the number of cells in the target traffic at the malicious exit onion router, the attacker can embed a secret signal into the variation of cell counter of the target traffic. The embedded signal will be carried along with the target traffic and arrive at the malicious entry onion router. Then, an accomplice of the attacker at the malicious entry onion router will detect the embedded signal based on the received cells and confirm the communication relationship among users. We have implemented this attack against Tor, and our experimental data validate its feasibility and effectiveness. There are several unique features of this attack. First, this attack is highly efficient and can confirm very short communication sessions with only tens of cells. Second, this attack is effective, and its detection rate approaches 100% with a very low false positive rate. Third, it is possible to implement the attack in a way that appears to be very difficult for honest participants to detect (e.g., using our hopping-based signal embedding).

In wireless sensor networks (WSNs), attackers could inject false data into the networks by compromising the sensor nodes. False data injected by the compromised nodes, if undetected, could not only cause false alarms but also consume the limited energy of the sensor nodes, posing serious threats to the lifetime of networks. To mitigate this type of attacks, a number of en-route filtering schemes to filter false data inside the networks have been developed in the past. However, there is lack of a systematical strategy to evaluate those schemes and establishing a foundation for designing en-route filtering techniques. To address these issues, we compare the pros and cons of the existing enroute filtering schemes. To fairly compare the performance of those schemes, we conduct theoretical analysis and derive a set of closed formulae for them. Our extensive simulations validate our findings. Our research summarizes the state-of-art research development and lay out future directions in this area.

The operating state estimation of power system is a critical process for providing a best-fit state estimation based on the meter measurements in the field and the configuration of power-grid network. To deal with the bad meter measurements caused by various faults in state estimation, power system researchers have developed numerous detection schemes in the past. Liu et al. recently proposed a new stealthy false data injection attack, which can bypass the existing bad data detection schemes and arbitrarily manipulate the states of power system, posing dangerous threats to the control of a power system. Nevertheless, their results did not show the detailed information of meters to be compromised. In this paper, we tend to tackle this issue and develop mechanisms to efficiently compute the optimal set of meter measurements given a number of state variables to be manipulated in a power-grid network. We formalize the problem of finding the optimal set of meter measurements as a known NP-hard problem and propose a heuristic approach to derive the near-optimal set of meter measurements efficiently. We implement our proposed scheme on the IEEE 9-bus, 14-bus, 30-bus, 118-bus and 300-bus systems and our data shows its efficiency and effectiveness.

Accurately measuring the available bandwidth information is critical for providing QoS assurance, especially for the bandwidth-limited 802.11-based wireless networks. However, the shared nature of wireless medium and IEEE 802.11 MAC pose great challenges for estimating the bandwidth accurately. This paper tends to tackle this issue. In particular, we first formally define the available bandwidth in IEEE 802.11 network by considering its unique characteristics. We then present our solution, Passive Available Bandwidth Estimation (PABE). In PABE, the effective link capacity is analyzed by considering the random factors in transmission, and the available channel idle time is estimated by passively monitoring the medium based on a new, lower threshold bandwidth obtained during the normal operations of IEEE 802.11. Our approach incurs very low cost to the network without any explicit message overhead. Through extensive simulation, our data validate that our approach consistently achieves much better performance than other existing algorithms in term of estimation accuracy.

Tor is a well-known low-latency anonymous communication system. To prevent the traffic analysis attack, Tor packs application data into equal-sized cells. However, we found that equal-sized cells at the application layer do not necessarily produce equal-sized packets at the network layer. Therefore, we introduced a packet size based attack that compromises Tor's communication anonymity with no need of controlling Tor routers. An attacker can manipulate size of packets between a web site and an exit onion router and embeds a signal into the target traffic. An accomplice at the user side can sniff the traffic and recognize this signal. To cope with the signal distortion incurred by Tor and Internet, we developed an effective signal recovery mechanism. Our real-world experiments validate the effectiveness of our attack against Tor. Our work demonstrates the need for re-considering the issue of padding anonymous communication data into equal size.

Anonymizer is a proprietary anonymous communication system. We discovered its architecture and found that the size of web packets through Anonymizer are very dynamic at the client. Motivated by this finding, we investigated a novel packet size based covert channel attack, against the anonymity service. In the attack, one attacker manipulates the web packet size between the web server and Anonymizer and embed signal symbols into the target traffic. An accomplice at the user side can sniff the traffic and recognize the secret signal. We developed intelligent and robust algorithms to cope with the packet size distortion incurred by Anonymizer and Internet. We developed several techniques to make the attack harder to detect: (i) We pick up right packets of web objects to manipulate in order to preserve the regularity of the TCP packet size dynamics; (ii) We adopt the Monte Carlo sampling technique to preserve the distribution of the web packet size despite manipulation. We have implemented the attack over Anonymizer and conducted extensive analysis and experimental evaluations. It is observed that the attack is highly efficient and requires only tens of packets to compromise the anonymous web surfing. The experimental results are consistent with our theoretical analysis.

Active worms pose major security threats to the Internet. This is due to the ability of active worms to propagate in an automated fashion as they continuously compromise computers on the Internet. Active worms evolve during their propagation, and thus, pose great challenges to defend against them. In this paper, we investigate a new class of active worms, referred to as Camouflaging Worm (C-Worm in short). The C-Worm is different from traditional worms because of its ability to intelligently manipulate its scan traffic volume over time. Thereby, the C-Worm camouflages its propagation from existing worm detection systems based on analyzing the propagation traffic generated by worms. We analyze characteristics of the C-Worm and conduct a comprehensive comparison between its traffic and nonworm traffic (background traffic). We observe that these two types of traffic are barely distinguishable in the time domain. However, their distinction is clear in the frequency domain, due to the recurring manipulative nature of the C-Worm. Motivated by our observations, we design a novel spectrum-based scheme to detect the C-Worm. Our scheme uses the Power Spectral Density (PSD) distribution of the scan traffic volume and its corresponding Spectral Flatness Measure (SFM) to distinguish the C-Worm traffic from background traffic. Using a comprehensive set of detection metrics and real-world traces as background traffic, we conduct extensive performance evaluations on our proposed spectrum-based detection scheme. The performance data clearly demonstrates that our scheme can effectively detect the C-Worm propagation. Furthermore, we show the generality of our spectrum-based scheme in effectively detecting not only the C-Worm, but traditional worms as well.

In mobile ad hoc networks, location service (LS) is critical and provides the fundamental service for geographic routing. However, most existing schemes for location service incur a high overhead because of the periodical updates of location information. In this paper, we intend to address this issue. Using the temporal relationship among historical locations of mobiles in the network, we propose a novel History information based Light Location Service (HLLS). In HLLS, location information of mobiles is propagated via Hello beacons locally, and location query is performed in a tracing manner with the aid of historical locations. In such a way, HLLS can eliminate the tremendous periodical location updates and significantly reduce the overhead for location service. Using extensive simulations, we demonstrate the effectiveness of HLLS in terms of high accuracy and low overhead.

In this paper, we present 3DLoc: an integrated system of hardware and software toolkits for locating an 802.11-compliant mobile device in a three dimensional (3D) space. 3DLoc features two specialized antennas: an azimuth antenna and an elevation antenna, for detecting the azimuth and elevation angles of a mobile device respectively in real time. To improve positioning accuracy in real-world urban settings, we propose various signal processing techniques such as clustering and wavelet-transform based denoising, and present theoretical analysis of the accuracy of these techniques. With different antenna configurations, 3DLoc is able to track single or multiple targets in one round of azimuth scanning and elevation scanning. We conduct extensive experiments to demonstrate the efficiency and accuracy of 3DLoc. 3DLoc can be used in various applications, including wireless network forensics for locating anonymous criminal mobile devices.

People are benefiting tremendously from pervasively deployed WiFi networks. However, criminals may exploit the anonymity of WiFi communication and wireless routers to access illegal content such as child porn videos. It's becoming an urgent topic as regards to how to preserve and acquire network forensic data from household and small business wireless routers in order to track down criminals. In this paper, we first survey the forensic capacity of nearly all household wireless routers which are available on market. We present our analysis for people who are willing to choose a wireless router to monitor their network. Secondly, we develop a generic network forensic data logging mechanism to monitor traffic into and out of wireless routers which support OpenWrt. Our code running in the wireless routers could log network traffic and send connection information to the administrator via email.

Abstract—Internet Threat Monitoring (ITM) systems are a widely deployed facility to detect, analyze, and characterize dangerous Internet threats such as worms and distributed denial-of-service (DDoS) attacks. Nonetheless, an ITM system can also become the target of attacks. In this paper, we address localization attacks against ITM systems in which an attacker impairs the effectiveness of an ITM system by identifying the locations of ITM monitors. We propose an information-theoretic framework that models localization attacks as communication channels. Based on this model, we generalize all existing attacks as “temporal attacks”, derive closed formulae of their performance, and propose an effective attack detection approach. The information-theoretic model also inspires a new attack called a spatial attack and motivates the corresponding detection approach. We show simulation results that support our theoretic findings.

In this paper, we address issues related to the modeling, analysis, and countermeasures of worm attacks on the Internet. Most previous work assumed that a worm always propagates itself at the highest possible speed. Some newly developed worms (e.g., “Atak” worm) contradict this assumption by deliberately reducing the propagation speed in order to avoid detection. As such, we study a new class of worms, referred to as self-disciplinary worms. These worms adapt their propagation patterns in order to reduce the probability of detection, and eventually, to infect more computers. We demonstrate that existing worm detection schemes based on traffic volume and variance cannot effectively defend against these self-disciplinary worms. To develop proper countermeasures, we introduce a game-theoretic formulation to model the interaction between the worm propagator and the defender. We show that an effective integration of multiple countermeasure schemes (e.g., worm detection and forensics analysis) is critical for defending against self-disciplinary worms. We propose different integrated schemes for fighting different self-disciplinary worms, and evaluate their performance via real-world traffic data.

We address issues related to establishing a defender's reputation in anomaly detection against two types of attackers: 1) smart insiders, who learn from historic attacks and adapt their strategies to avoid detection/punishment, and 2) naïve attackers, who blindly launch their attacks without knowledge of the history. In this paper, we propose two novel algorithms for reputation establishment--one for systems solely consisting of smart insiders and the other for systems in which both smart insiders and naïve attackers are present. The theoretical analysis and performance evaluation show that our reputation-establishment algorithms can significantly improve the performance of anomaly detection against insider attacks in terms of the tradeoff between detection and false positives.

Various low-latency anonymous communication systems such as Tor and Anoymizer have been designed to provide anonymity service for users. In order to hide the communication of users, many anonymity systems pack the application data into equal-sized cells (e.g., 512 bytes for Tor, a known real-world, circuit-based low-latency anonymous communication network). In this paper, we investigate a new cell counter based attack against Tor, which allows the attacker to confirm anonymous communication relationship among users very quickly. In this attack, by marginally varying the counter of cells in the target traffic at the malicious exit onion router, the attacker can embed a secret signal into the variation of cell counter of the target traffic. The embedded signal will be carried along with the target traffic and arrive at the malicious entry onion router. Then an accomplice of the attacker at the malicious entry onion router will detect the embedded signal based on the received cells and confirm the communication relationship among users. We have implemented this attack against Tor and our experimental data validate its feasibility and effectiveness. There are several unique features of this attack. First, this attack is highly efficient and can confirm very short communication sessions with only tens of cells. Second, this attack is effective and its detection rate approaches 100% with a very low false positive rate. Third, it is possible to implement the attack in a way that appears to be very difficult for honest participants to detect (e.g. using our hopping-based signal embedding).

"The Marauder's Map" is a magical map in J. K. Rowling's fantasy series, "Harry Potter and the Prisoner of Azkaban". It shows all moving objects within the boundary of the "Hogwarts School of Witchcraft and Wizardry". In this paper, we introduce a similar attack to location privacy in wireless networks. Our system, namely the digital Marauder's map, can reveal the locations of WiFi-enabled mobile devices within the coverage area of a single high-gain antenna. The digital Marauder's map is built solely with off-the-shelf wireless equipments, and features a mobile design that can be quickly deployed to a new location and instantly used without training. We present a comprehensive set of theoretical analysis and experimental results which demonstrate the coverage and localization accuracy of the digital Marauder's map.

We address issues related to privacy protection in location-based services (LBS). Most existing research in this field either requires a trusted third-party (anonymizer) or uses oblivious protocols that are computationally and communicationally expensive. Our design of privacy-preserving techniques is principled on not requiring a trusted third-party while being highly efficient in terms of time and space complexities. The problem has two interesting and challenging characteristics: First, the degree of privacy protection and LBS accuracy depends on the context, such as population and road density, around a user's location. Second, an adversary may violate a user's location privacy in two ways: (i) based on the user's location information contained in the LBS query payload, and (ii) by inferring a user's geographical location based on its device's IP address. To address these challenges, we introduce CAP, a Context-Aware Privacy-preserving LBS system with integrated protection for data privacy and communication anonymity. We have implemented CAP and integrated it with Google Maps, a popular LBS system. Theoretical analysis and experimental results validate CAP's effectiveness on privacy protection, LBS accuracy, and communication Quality-of-Service.

Recently, the direct sequence spread-spectrum (DSSS)-based technique has been proposed to trace anonymous network flows. In this technique, homogeneous pseudo-noise (PN) codes are used to modulate multiple-bit signals that are embedded into the target flow as watermarks. This technique could be maliciously used to degrade an anonymous communication network. In this paper, we propose a simple single flow-based scheme to detect the existence of these watermarks. Our investigation shows that even if we have no knowledge of the applied PN code, we are still able to detect malicious DSSS watermarks via mean-square autocorrelation (MSAC) of a single modulated flow's traffic rate time series. MSAC shows periodic peaks due to self-similarity in the modulated traffic caused by homogeneous PN codes that are used in modulating multiple-bit signals. Our scheme has low complexity and does not require any PN-code synchronization. We evaluate this detection scheme's effectiveness via simulations and real-world experiments on Tor. Our results demonstrate a high detection rate with a low false positive rate. Our scheme is more flexible and accurate than an existing multi-flow-based approach in DSSS watermark detection.

Anonymity technologies such as mix networks have gained increasing attention as a way to provide communication privacy. Mix networks were developed for message-based applications such as e-mail, but researchers have adapted mix techniques to low-latency flow-based applications such as anonymous Web browsing. Although a significant effort has been directed at discovering attacks against anonymity networks and developing countermeasures to those attacks, there is little systematic analysis of the quality of service (QoS) for such security and privacy systems. In this paper, we systematically address TCP performance issues of flow-based mix networks. A mix's batching and reordering schemes can dramatically reduce TCP throughput due to out-of-order packet delivery. We developed a theoretical model to analyze such impact and present formulas for approximate TCP throughput in mix networks. To improve TCP performance, we examined the approach of increasing TCP's duplicate threshold parameter and derived formulas for the performance gains. Our proposed approaches will not degrade the system anonymity degree since they do not change the underlying anonymity mechanism. Our data matched our theoretical analysis well. Our developed theoretical model can guide the deployment of batching and reordering schemes in flow-based mix networks and can also be used to investigate a broad range of reordering schemes.

Internet threat monitoring (ITM) systems have been deployed to detect widespread attacks on the Internet in recent years. However, the effectiveness of ITM systems critically depends on the confidentiality of the location of their monitors. If adversaries learn the monitor locations of an ITM system, they can bypass the monitors and focus on the uncovered IP address space without being detected. In this paper, we study a new class of attacks, the invisible LOCalization (iLOC) attack. The iLOC attack can accurately and invisibly localize monitors of ITM systems. In the iLOC attack, the attacker launches low-rate port-scan traffic, encoded with a selected pseudonoise code (PN-code), to targeted networks. While the secret PN-code is invisible to others, the attacker can accurately determine the existence of monitors in the targeted networks based on whether the PN-code is embedded in the report data queried from the data center of the ITM system. We formally analyze the impact of various parameters on attack effectiveness. We implement the iLOC attack and conduct the performance evaluation on a real-world ITM system to demonstrate the possibility of such attacks. We also conduct extensive simulations on the iLOC attack using real-world traces. Our data show that the iLOC attack can accurately identify monitors while being invisible to ITM systems. Finally, we present a set of guidelines to counteract the iLOC attack.

Internet threat monitoring (ITM) systems are a widely deployed facility to detect, analyze, and characterize dangerous Internet threats such as worms and distributed denial-of-service (DDoS) attacks. Nonetheless, an ITM system can also become the target of attack. In this paper, we address localization attacks against ITM systems in which an attacker impairs the effectiveness of ITM systems by identifying the locations of ITM monitors. We propose an information-theoretic framework for the modeling of localization attacks as communication channels. Based on the information-theoretic model, we generalize all existing attacks as ldquotemporal attacksrdquo, derive closed formulae of their performance, and propose an effective detection approach. The information-theoretic model also inspires a new attack called a spatial attack and motivates the corresponding detection approach. We show simulation results that support our theoretic findings.

The surge of peer-to-peer (P2P) networks consisting of thousands of of hosts makes them a breeding ground for malware proliferation. Although some existing studies have shown that malware proliferation can pose significant threats to P2P networks, defending against such an attack is largely an open problem. This paper aims to develop the countermeasure that can effectively mitigate the malware proliferation while preserving P2P networks' performance. To this end, we propose a dynamic trust management scheme based upon localized trust evaluation and alert propagation which prevents innocent peers from downloading files from infected peers. Our analysis and experimental results show that our approach can effectively reduce the malware proliferation rate.

Tor is a real-world, circuit-based low-latency anonymous communication network, supporting TCP applications on the Internet. In this paper, we present a new class of attack, the replay attack, against Tor. Compared with other existing attacks, the replay attack can confirm communication relationships quickly and accurately and poses a serious threat against Tor. In this attack, a malicious entry onion router duplicates cells of a stream from a sender. The original cell and duplicate cell traverse middle onion routers and arrive at an exit onion router along a circuit. Since Tor uses the counter mode AES (AES-CTR) for encryption of cells, the duplicate cell disrupts the normal counter at middle and exit onion routers and the decryption at the exit onion router incurs cell recognition errors. If an accomplice of the attacker at the entry onion router controls the exit onion router and detects such decryption errors, the communication relationship between the sender and receiver will be discovered. The replay attack can also be used as a denial of service attack. We implement the replay attack on Tor and our experiments validate the feasibility and effectiveness of the attack. We also present guidelines to defending against the replay attack.

We address issues related to the establishment of defender's reputation in anomaly detection against insider attacks. We consider two types of attackers: smart insiders, which learn from historic attacks and adapt their strategies to avoid detection/punishment, and naive attackers, which blindly launch their attacks. We introduce two novel reputation-establishment algorithms for systems with solely smart insiders and systems with both smart insiders and naive attackers, respectively. Theoretical analysis and simulation results show that our reputation-establishment algorithms can significantly improve the performance of anomaly detection against insider attacks in terms of the tradeoff between detection and false positives.

In this paper, we study a new class of attacks, theinvisibleLOCalization (iLOC) attack, which can accurately and invisibly localize monitors of Internet threat monitoring (ITM) systems, a class of widely deployed facilities to characterize Internet threats, such as worm propagation, denial-of-service (DoS) attacks. In theiLOCattack, the attacker launches low-rate port-scan traffic, encoded with a selectedpseudo-noisecode(PN- code), to targeted networks. While the secret PN-code is invisible to others, the attacker can accurately determine the existence of monitors in the targeted networks based on whether the PN-code is embedded in the report data queried from the data center of the ITM system. We conduct extensive simulations on theiLOCattack using real-world traces. Our data demonstrate that theiLOCattack can accurately identify monitors while remaining invisible to the ITM. Finally, we present a set of guidelines to counteract theiLOCattack.

Although a significant amount of effort has been directed at discovering attacks against anonymity communication networks and developing countermeasures to those attacks, there is little systematic analysis of the Quality of Service (QoS) for such privacy preserving systems. In this paper, we initiate the effort to study the QoS of Tor, a popular and representative implementation of anonymous communication networks on the Internet. We find that Tor suffers severe TCP performance degradation because of its random path selection strategy. Our investigation shows that Tor’s bandwidth weighted path selection algorithm can only improve the performance to a very limited extent. We analyze this performance issue from the perspective of overlay networks and model the TCP throughput of Tor. We conduct extensive experiments on the real-world Tor network and the experimental results validate our theory. We also discuss possible remedies to this performance issue.

Anonymity technologies such as mix networks have gained increasing attention as a way to provide communication privacy. Mix networks were developed for message-based applications such as email, but researchers have adapted mix techniques to low-latency, flow-based applications such as anonymous web browsing. In this paper, we systematically address TCP performance issues of flow-based mix networks. We explain why a mix's batching and reordering schemes can dramatically reduce TCP throughput due to out-of-order packet delivery. We developed a theoretical model to analyze such impact and present closed formulae for TCP throughput in mix networks. To improve TCP performance, we examined the approach of increasing TCP's duplicate threshold parameter and derived closed formulae for the performance gains. Our simulation results matched our theoretical analysis well.

Law enforcement agencies need the ability to conduct electronic surveillance to combat crime, terrorism, or other malicious activities exploiting the Internet. However, the proliferation of anonymous communication systems on the Internet has posed significant challenges to providing such traceback capability. In this paper, we develop a new class of flow marking technique for invisible traceback based on direct sequence spread spectrum (DSSS), utilizing a pseudo-noise (PN) code. By interfering with a sender's traffic and marginally varying its rate, an investigator can embed a secret spread spectrum signal into the sender's traffic. The embedded signal is carried along with the traffic from the sender to the receiver, so the investigator can recognize the corresponding communication relationship, tracing the messages despite the use of anonymous networks. The secret PN code makes it difficult for others to detect the presence of such embedded signals, so the traceback, while available to investigators is, effectively invisible. We demonstrate a practical flow marking system which requires no training, and can achieve both high detection and low false positive rates. Using a combination of analytical modeling, simulations, and experiments on Tor (a popular Internet anonymous communication system), we demonstrate the effectiveness of the DSSS-basedflow marking technique.

Active worms pose major security threats to the Internet. In this paper, we investigate a new class of active worms, i.e., camouflaging worm (C-Worm in short). The C-Worm has the capability to intelligently manipulate its scan traffic volume over time, thereby camouflaging its propagation from existing worm detection systems. We analyze characteristics of the C-Worm and conduct a comprehensive comparison between its traffic and non-worm traffic. We observe that these two types of traffic are barely distinguishable in the time domain, however, their distinction is clear in the frequency domain, due to the recurring manipulative nature of the C-Worm. Motivated by our observations, we design a novel spectrum-based scheme to detect the C-Worm. Our scheme uses the power spectral density (PSD) distribution of the scan traffic volume and its corresponding spectral flatness measure (SFM) to distinguish the C-Worm traffic from non-worm traffic. We conduct extensive performance evaluations on our proposed detection scheme against the C-Worm. The performance data clearly demonstrates that our proposed scheme can effectively detect the C-Worm propagation

Honeypots are decoys designed to trap, delay, and gather information about attackers. We can use honeypot logs to analyze attackers' behaviors and design new defenses. A virtual honeypot can emulate multiple honeypots on one physical machine and provide great flexibility in repesenting one or more networks of machines. But when attackers recognize a honeypot, it becomes useless. In this paper, we address issues related to detecting and "camouflaging" virtual honeypots, in particular Honeyd, which can emulate any size of network on physical machines. We find that an attacker may remotely fingerprint Honeyd by measuring the latency of the network links emulated by Honeyd. We analyze the threat from this fingerprint attack based on the Neyman-Pearson decision theory and find that this class of attack can achieve a high detection rate and low false alarm rate. In order to counter this fingerprint attack, we make virtual honeypots behave like their surrounding networks and blend in with their surroundings. We design a camouflaged Honeyd by revising a small part of the Honeyd toolkit code and by appropriately patching the operating system. Our experiments demonstrate the effectiveness of our approach to camouflaging Honeyd.

This paper proposes a dynamic geographic hash table for data-centric storage (DCS) in sensor networks. In DCS systems, data storage locations are determined by data name. The storage locations are obtained through the use of a geographic hash table (GHT) that maps data names to geographic locations. Traditional DCS systems use a static hash function for this purpose, resulting in a static set of nodes serving the network throughout its lifetime. Hence, these nodes may experience unbalanced resource utilization problems and the network will not be capable of dealing with network dynamics such as new sensor deployments or runtime sensor failures. We address these problems by proposing a dynamic GHT solution that relies on two schemes: 1) a temporal-based geographic hash table to achieve overall load balancing among sensor nodes over time; and 2) a location selection scheme based on node contribution potential to proactively adapt the system to network dynamics. Our performance evaluations show that the dynamic GHT can alleviate the resource utilization problem of DCS systems and can prolong the network lifetime significantly

Sensor nodes being small in size and distributively deployed, are vulnerable to physical attacks that attempt to physically destroy sensors in the sensor network. Generally speaking, physical attacks in sensor networks can be classified into two types: blind physical attacks and search-based physical attacks. In blind attacks, sensors are destroyed using brute-force approaches (like bombs/grenades etc.). The advantage here is the rapidness in destroying sensors. The downside however, is the fact that the deployment field also suffers significant casualties. If the attacker wishes to preserve the deployment field, the attacker will conduct search-based attacks by searching for sensors in the field and destroying only the sensors. While this preserves the deployment field, the attack process is slow. In this paper, we present policy-driven physical attacks, where the bias between the twin objectives of the attacker (rapidly destroying sensors, and preserving the deployment field) is modeled as a policy for the attacker. In policy-driven physical attacks, the attacker walks through the sensor network deployment field using signal detecting equipment to locate active sensors. Depending on the attacker's policy, the attacker takes different actions during the attack process. Based on detailed performance measurement, we observe that the policy has impacts on the network performance and destruction in the deployment field, demonstrating that the attacker can achieve desired bias in its objectives under policy-driven physical attacks

In a large-scale sensor network, the amount of data collected increases rapidly over time. Efficient data storage and retrieval in sensor networks is therefore critical to enhancing network lifetime. In this paper, we propose an adaptive zone-based storage architecture for sensor networks. Our design consists of three major components: 1) a distributed clustering algorithm that operates on a novel concept of node contribution potential, allowing the network to be clustered based on desired performance, 2) a local storage policy to reduce cross-network transmission which is resilient to node and network failures, and 3) an overlay of zone leaders to distribute queries and data efficiently. In cases where the query sources are limited to a number of fixed points in the network, we propose a query source adaptation policy to reduce network stress on overlay links. The strengths of our architecture include the ability to customize the network's desired performance at run time, its low protocol overhead, scalability and resilience to network failures.

Recent active worm propagation events show that active worms can spread in an automated fashion and flood the Internet in a very short period of time. Our previous results show that P2P systems with large number of hosts can be a potential vehicle for the active worm attacker to achieve fast worm propagation in the Internet. In this paper, we propose a region-based active immunization defense strategy in P2P systems to fight against P2P-based active worm attacks. We develop an analytical approach to evaluate the efficiency of our proposed defense strategy. Our numerical analysis results show that: although P2P-based attacks can significantly improve the attack performance by attacking vulnerable P2P systems, our proposed defense strategy can effectively slow down the worm propagation. We also observe that defense parameters such as defense list size, worm detection success ratio, and immunization rate have significant impacts on the performance of our defense strategy.

Recent active worm propagation events show that active worms can spread in an automated fashion and flood the Internet in a very short period of time. Due to the recent surge of peer-to-peer (P2P) systems with large numbers of users, P2P systems can be a potential vehicle for the active worms to achieve fast worm propagation in the Internet. In this paper, we address the issue of the impacts of active worm propagation on top of P2P systems. In particular: (1) we define a P2P system based active worm attack model and study two attack strategies (an off-line and on-line strategy) under the defined model; (2) we develop an analytical approach to analyze the propagation of active worms under the defined attack models and conduct an extensive study to the impacts of P2P system parameters, such as size, topology degree, and the structured/unstructured properties on active worm propagation. Based on numerical results, we observe that a P2P-based attack can significantly worsen attack effects (improve attack performance), and we observe that the speed of worm propagation is very sensitive to P2P system parameters. We believe that our work can provide important guidelines in design and control of P2P systems as well as overall active worm defense.

The small form factor of the sensors, coupled with the unattended and distributed nature of their deployment expose sensors to physical attacks that physically destroy sensors in the network. In this paper, we study the modeling and analysis of search-based physical attacks in sensor networks. We define a search-based physical attack model, where the attacker walks through the sensor network using signal detecting equipment to locate active sensors, and then destroys them. We consider both flat and hierarchical sensor networks. The attacker in our model uses a weighted random selection based approach to discriminate multiple target choices (normal sensors and cluster-heads) to enhance sensor network performance degradation. Our performance metric in this paper is accumulative coverage (AC), which effectively captures coverage and lifetime of the sensor network. We then conduct detailed evaluations on the impacts of search-based physic attacks on sensor network performance. Our performance data clearly show that search-based physical attacks significantly reduce sensor network performance. We observe that attack related parameters, namely attacker movement speed, detection range and accuracy have significant impacts on the attack effectiveness. We also observe that the attack effectiveness is significantly impacted by sensor network parameters, namely the frequency of communication and frequency of cluster-head rotation. We believe that our work in this paper on modeling and analyzing search-based physical attacks is an important first step in understanding their overall impacts, and effectively defending against them in the future.

Recent active worm propagation events show that active worms can spread in an automated fashion and flood the Internet in a very short period of time. We address the issue by analyzing the impacts of worm attack approaches on Internet worm propagation. In particular, we analyze four worm attack approaches with considering following attack strategies: pure random-based/ PlP (peer-to-peer) hitlist-based strategies for victim selection and cooperation-based/ non-cooperation-based strategies for worm instance coordination. Our numerical analysis results show that the attack approach with combining the worm instance cooperation and P2P hitlist-based victim selection strategies achieves the best performance compared to all other attack approaches in term of attack efficiency.

Providing efficient data services is one of the fundamental requirements for wireless sensor networks. The data service paradigm requires that the application submit its requests as queries and the sensor network transmits the requested data to the application. While most existing work in this area focuses on data aggregation, not much attention has been paid to query aggregation. For many applications, especially ones with high query rates, query aggregation is very important. We study a query aggregation-based approach for providing efficient data services. In particular: (1) we propose a multi-layered overlay-based framework consisting of a query manager and access points (nodes), where the former provides the query aggregation plan and the latter executes the plan; (2) we design an effective query aggregation algorithm to reduce the number of duplicate/overlapping queries and save overall energy consumption in the sensor network Our performance evaluations show that by applying our query aggregation algorithm, the overall energy consumption can be significantly reduced and the sensor network lifetime can be prolonged correspondingly.

IP communication service over Internet has become most interesting research topic in recent years, as the next generation Internet applications are requiring the integration of voice, video and data in the single IP infrastructure. In this study, we focus on designing a scalable voice over IP routing architecture from the network control plan consideration, aiming at dealing with issues related to the global deployment of voice over IP service. Particularly, our technologies include: 1) Peer-to-Peer based call routing architecture. Peer-to-Peer approach can fully supports the variable network topology to satisfy the transition period of global voice over IP deployment in the next several years. We study the routing request forwarding protocol and routing update protocol with designing an efficient multipath routing algorithms to achieve better QoS (Quality of Service) performance. 2) Efficient voice gateway allocation schemes. When the call routing fails in the IP overlay, the call setup can automatically reroute to the PSTN (public switched telephone network) and fault tolerant service can be easily extended. We study how to support differentiated service to satisfy different user requirements. We conduct extensive performance evaluations on different architecture and algorithms. The evaluation results show that the load aware routing scheme can achieve much better performance than static selection scheme in terms of average call routing message delay. The experiment results also demonstrate that the weight based gateway allocation scheme can efficiently support call requests with different QoS requirements.