Abstract The widespread adoption of deep learning (DL) libraries has raised concerns about their reliability and security. While prior works leveraged large language models (LLMs) to generate test programs for DL library APIs, the hardcoded program behaviors and low code validity rates render them impractical for real-world testing. To address these challenges, we propose FD-FACTORY, a fully automated framework that leverages LLMs to generate fuzz drivers for DL API testing. The fuzz driver programs accept mutated inputs from fuzzing engines to achieve effective code analysis. Inspired by the modular design of industrial production lines, FD-FACTORY decomposes the generation process into eight distinct stages: Preparation, Initial Fuzz Driver Generation, Early Stop Checks, Verification, Issue Diagnosis, Decision Making, Repair Loop, and Deployment . Each stage is handled by dedicated agents or tools to enhance construction efficiency. Experimental results demonstrate that FD-FACTORY achieves 73.67% and 65.33% success rates in generating fuzz drivers for PyTorch and TensorFlow, producing an improvement of 34.66 to $$-$$ - 54.66% than existing approaches. In addition, FD-FACTORY provides more comprehensive coverage tracking by supporting both Python and native C/C code. It achieves a total coverage of 308,351 lines on PyTorch and 528,427 lines on TensorFlow, substantially surpassing the results reported by previous approaches. Unlike prior approaches relying on repeated interactions with the LLM servers throughout the entire testing process, our framework confines the use of LLMs strictly to the fuzz driver generation stages before deployment. Once generated, the fuzz drivers can be reused without further LLM involvement, thereby enhancing the practicality and sustainability of LLM-assisted fuzzing in real-world scenarios.

Autonomous agents utilizing Large Language Models (LLMs) have demonstrated remarkable capabilities in isolated medical tasks like diagnosis and image analysis, but struggle with integrated clinical workflows that connect diagnostic reasoning and medication decisions. We identify a core limitation: existing medical AI systems process tasks in isolation without the cross-validation and knowledge integration found in clinical teams, reducing their effectiveness in real-world healthcare scenarios. To transform the isolation paradigm into a collaborative approach, we propose MedCoAct, a confidence-aware multi-agent framework that simulates clinical collaboration by integrating specialized doctor and pharmacist agents, and present a benchmark, DrugCareQA, to evaluate medical AI capabilities in integrated diagnosis and treatment workflows. Our results demonstrate that MedCoAct achieves 67.58% diagnostic accuracy and 67.58% medication recommendation accuracy, outperforming single agent framework by 7.04% and 7.08% respectively. This collaborative approach generalizes well across diverse medical domains, proving especially effective for telemedicine consultations and routine clinical scenarios, while providing interpretable decision-making pathways.

Deep Neural Networks (DNNs) are vulnerable to backdoor attacks. In these attacks, an adversary inserts backdoor triggers during the training phase, causing misclassification of the model during inference stage when the triggers present. These hidden vulnerabilities pose significant risks, particularly in critical applications. To address this issue, we propose a novel One-Class Activation Graph Embedding (OCAGE) method. It is a one-class classification method that leverages Graph Neural Networks (GNNs) to detect backdoor samples at the input level, requiring only a small clean dataset for effective sample detection. First, we design an efficient model-to-graph technique to extract the outputs of model forward propagation, specifically activation values. Then, we combine self-supervised generative graph pretraining with a one-class classifier to distinguish poisoned samples from clean ones. Our proposed OCAGE method leverages the representational power of GNNs to extract activation features of inputs, highlighting deviations in activation patterns between normal and malicious cases. Furthermore, OCAGE employs a one-class classifier trained exclusively on benign samples, ensuring the condition the model owner without requiring access to malicious examples during training. The experiments demonstrate that OCAGE significantly outperforming existing methods.

Over the past few years, the emergence of backdoor attacks has presented significant challenges to deep learning systems, allowing attackers to insert backdoors into neural networks. When data with a trigger is processed by a backdoor model, it can lead to mispredictions targeted by attackers, whereas normal data yields regular results. The scope of backdoor attacks is expanding beyond computer vision and encroaching into areas such as natural language processing and speech recognition. Nevertheless, existing backdoor defense methods are typically tailored to specific data modalities, restricting their application in multimodal contexts. While multimodal learning proves highly applicable in facial recognition, sentiment analysis, action recognition, visual question answering, the security of these models remains a crucial concern. Specifically, there are no existing backdoor benchmarks targeting multimodal applications or related tasks.

Digital twin plays an important role on realizing the digitization and intelligence of the smart engine for the maritime intelligent transportation systems. However, digital twin modeling of the engine systems faces the challenges of multidisciplinary knowledge, multi-scale, real time, complex structure, etc. In this study, the hardware-in-the-loop technology is introduced into the digital twin modeling processes. A six-dimension digital twin model framework is proposed, which contains physical entity, hardware-in-the-loop, virtual equipment, application service, digital twin data, and connections. The digital twin framework is described in detail for the engine system. The application of the hardware-in-the-loop in the digital twin modeling is further expounded with the example of a single-cylinder engine test bench. The new digital twin model framework and modeling method will be benefit for the construction of digital twin for complex systems such as power plants, vehicles, ships, etc.

In recent years, attention-based models have excelled across various domains but remain vulnerable to backdoor attacks, often from downloading or fine-tuning on poisoned datasets. Many current methods to mitigate backdoors in NLP models rely on the pre-trained (unfine-tuned) weights, but these methods fail in scenarios where the pre-trained weights are not available. In this work, we propose MBTSAD, which can mitigate backdoors in the language model by utilizing only a small subset of clean data and does not require pre-trained weights. Specifically, MBTSAD retrains the backdoored model on a dataset generated by token splitting. Then MBTSAD leverages attention distillation, the retrained model is the teacher model, and the original backdoored model is the student model. Experimental results demonstrate that MBTSAD achieves comparable backdoor mitigation performance as the methods based on pretrained weights while maintaining the performance on clean data. MBTSAD does not rely on pre-trained weights, enhancing its utility in scenarios where pre-trained weights are inaccessible. In addition, we simplify the min-max problem of adversarial training and visualize text representations to discover that the token splitting method in MBTSAD's first step generates Out-of-Distribution (OOD) data, leading the model to learn more generalized features and eliminate backdoor patterns.

As artificial intelligence advances, deep learning and machine vision technologies have been widely applied in unmanned aerial vehicle (UAV) platforms for tasks such as target tracking and visual avoidance. The reliability of drones equipped with AI models is critically dependent on the security of these models. The training of AI models often requires substantial computational resources and typically relies on third-party platforms for computational power, datasets, and pre-trained models. This reliance creates opportunities for AI backdoor attacks, posing security risks to AI-powered drones. This paper introduces a novel undirected physical backdoor attack method, PUBA, that utilizes target disappearance and false target generation to poison labels. By integrating data poisoning techniques, PUBA implements backdoor attacks in physical space, collects datasets in the real world, trains backdoor models, and evaluates the effectiveness, stealth, and robustness of PUBA in both simulated systems and real drones. This study disrupts the normal execution of target recognition and tracking tasks on drones by poisoning data and implanting backdoors in target recognition models used by UAVs, exposing potential security vulnerabilities in the domain of drone target tracking.

Deep Neural Networks (DNNs) have been found vulnerable to backdoor attacks, raising security concerns about their deployment in mission-critical applications. While numerous methods exist to detect backdoor attacks, many of them rely on prior knowledge of the attacks to be detected and require a certain amount of backdoor samples for training, which limits their application in real-world scenarios. This study introduces a novel One-Class Graph Embedding Classification (OCGEC) framework using Graph Neural Networks (GNNs) for model-level backdoor detection. OCGEC first trains a large number of tiny models with a small amount of clean data, then converts these models into graphs to leverage their structural information and weights. A generative self-supervised Graph Auto-Encoder (GAE) is pre-trained on these graphs to learn the representation of DNNs. It is further combined with one-class classification optimization objectives to form a classification boundary between backdoor and benign models, which can effectively detect backdoor models without any knowledge of the attack strategy. Experiments show that our OCGEC achieves AUC scores of more than 98% against state-of-the-art backdoor attacks on various datasets, outperforming existing backdoor detection methods with a distinctive edge in performance. Note that OCGEC only needs a small amount of clean data and does not rely on any knowledge of the backdoor attacks, making it well-suited for real-world applications.

Backdoor attacks against deep learning based object detectors have been studied increasingly in recent years. While most proposed attacks primarily focus on compromising the model’s integrity by inducing incorrect detections, only few studies explore backdoor attacks targeting the model’s availability, a critical concern in safety-critical domains such as autonomous driving. In this paper, we introduce a novel backdoor attack called the Sponge Backdoor Attack (SBA), designed to increase the detection latency of end-to-end object detectors. Specifically, we overload a commonly employed technique in many object detectors - non-maximum suppression (NMS) by introducing a large amount of non-existent objects. Through comprehensive experiments, we demonstrate the SBA’s effectiveness to prolong the processing time of the poisoned image while maintaining detection performance on clean images across various models, datasets, and hardware platforms.

At present, there is less research on the detection of broken access control vulnerabilities in IoT systems, mostly using state machines to analyze abnormal state transitions, and no systematic tools have been developed. The main challenges include the inaccessibility of communication messages, a lack of effective detection for broken access control vulnerabilities, and excessive manual involvement. Moreover, due to the existence of encryption, signatures, and other fields, it is challenging to directly port web-based detection tools to IoT. In response to these challenges, we propose a framework for detecting broken access control vulnerabilities based on the interaction between applications and cloud platforms. The framework employs man-in-the-middle techniques to obtain communication messages between the two entities, enabling fast and effective fuzz testing through keyword extraction, database-guided fuzzing, and response-based detection algorithms. In addition, a combination of dynamic and static reverse analysis techniques are used to overcome anti-tampering measures, such as encryption and signatures. Following the detection framework, we implemented the semi-automated BACDetector system and tested it on six applications from four manufacturers. BACDetector discovered nine broken access control vulnerabilities, including risks of device hijacking and privacy leakage. This validated its effectiveness in detecting vulnerabilities in IoT.

With the development of software vulnerability analysis, the evaluation of different bug-detecting tools has become quite important for selecting better-performed ones and improving existing approaches. To obtain a convincing evaluation result, a well-constructed vulnerability corpus is indispensable. However, the existing corpora are either constructed from real-world bugs or artificially designed, suffering various problems like small volume, lack of ground truth, etc. Thus, generating large-scale bug corpora through an automated way has been widely noticed. In this paper, we propose an automated vulnerability injection system to generate code samples with triggerable vulnerabilities. Specifically, the system analyzes a host program with the symbolic execution tool to generate high-coverage test cases. Meanwhile, it identifies the potential bug injection points and performs static taint analysis to mark tainted variables and their relevance to the bug injection points. Based on the variables, the system modifies the host program to vulnerable code samples that could be verified by the test cases. In conclusion, the system realizes the injection of buffer overflow vulnerabilities in $\mathrm{C}/ \mathrm{C}++$ programs. A study case is shown to demonstrate the injection processes, and the evaluation presents our advantages in the realness and magnitude of generated bug samples as well as solving highcoverage test cases.

With the rapid development of artificial intelligence in recent years, the deep neural network model has been used in many fields such as speech and images due to its excellent performance, and has achieved remarkable results. As we all know, training a deep model requires a lot of time and resources. But these trained deep learning models are very easy to be copied and diffused. Therefore, the protection of intellectual property rights of the model has gradually attracted people’s attention. A series of algorithms or technologies came into being, and one of them is model watermarking technology. Model watermarks can function like digital watermarks. Once the model is stolen, watermarks can prove the copyright of model by verifying the watermarks, maintain its intellectual property rights, and protect the model. This paper proposes a model watermark generation method based on pruning. Where to prune is selected by the calculation result of connection sensitivity, and then the information is embedded by pruning. Compared with the four proposed model watermarking methods, our method has higher fidelity and reliability. Experiments show that our watermarking method is robust against fine-tuning and weight pruning.

Poker is the typical game of incomplete information, and remains a longstanding challenge problem in artificial intelligence (AI). The poker game of Dou Dizhu has been viewed as a thorny topic in AI because of its own characteristics. This article introduces a developed Monte Carlo tree search (MCTS) method for Dou Dizhu to solve the decision making effectively. We built the winning rate prediction model (WRPM) to predict the winning rate of moves as the initial situation estimation and improve the model to be more applicable to different player roles. Then, the WRPM is embedded as the core algorithm into MCTS for extension and simulation and named it WRPM-MCTS. In addition, we also train a card distribution prediction model to predict the holding cards of opponents for further improving the performance of WRPM-MCTS on the agent of Dou Dizhu. Experiments show that the WRPM-MCTS has a statistically significant performance better than the pure MCTS and the pure WRPM. In the game with human players from an online game platform, the WRPM-MCTS-based agent had the winning rate of 52.86% in 4 000 000 games and ranked in top 1.22% among 500 000 human players, indicating that this agent had reached the expert level of humans.

Nowadays, Bitcoin has become the most popular cryptocurrency, which gains the attention of investors and speculators alike. Asset pricing is a risky and challenging activity that enchants lots of shareholders. Indeed, the difficulty in making predictions lies in understanding the multiple factors that affect the Bitcoin price trend. Modeling the market behavior and thus, the sentiment in the Bitcoin ecosystem provides an insight into the predictions of the Bitcoin price. While there are significant studies that investigate the token economics based on the Bitcoin network, limited research has been performed to analyze the network sentiment on the overall Bitcoin price. In this paper, we investigate the predictive power of network sentiments and explore statistical and deep-learning methods to predict Bitcoin future price. In particular, we analyze financial and sentiment features extracted from economic and crowd-sourced data respectively, and we show how the sentiment is the most significant factor in predicting Bitcoin market stocks. Next, we compare two models used for Bitcoin time-series predictions: the Auto-Regressive Integrated Moving Average with eXogenous input (ARIMAX) and the Recurrent Neural Network (RNN). We demonstrate that both models achieve optimal results on new predictions, with a mean squared error lower than 0.14%, due to the inclusion of the studied sentiment feature. Besides, since the ARIMAX achieves better predictions than the RNN, we also prove that, with just a linear model, we may obtain outstanding market forecasts in the Bitcoin scenario.

In recent years, more and more anonymous network have been developed. Since user's identity is difficult to trace in anonymous networks, many illegal activities are carried out in darknet. In this paper, we propose a hierarchical classifier of darknet traffic which can distinguish four types of darknet(Tor, I2P, ZeroNet, Freenet) and 25 darknet users' behavior. Due to the lack of public datasets, we deployed a darknet data probe that can capture real darknet traffic in Tor, I2P, ZeroNet, Freenet. After collecting and labeling darknet traffic, we extract 26 time-based flow features that can represent the characteristics of darknet traffic and train a hierarchical classifier constructed by 6 local classifiers. Results show that the classifier can easily distinguish Tor, I2P, ZeroNet, Freenet four kinds of darknet clients with an accuracy of 96.9% and identify 8 kinds of user behaviors for each type of darknet with an accuracy of 91.6% on average. With the help of this hierarchical classification method, darknet user behaviors can be accurately distinguished at the traffic exit.

The password has become today's dominant method of authentication. While brute-force attack methods such as HashCat and John the Ripper have proven unpractical, the research then switches to password guessing. State-of-the-art approaches such as the Markov Model and probabilistic context-free grammar (PCFG) are all based on statistical probability. These approaches require a large amount of calculation, which is time-consuming. Neural networks have proven more accurate and practical in password guessing than traditional methods. However, a raw neural network model is not qualified for cross-site attacks because each dataset has its own features. Our work aims to generalize those leaked passwords and improves the performance in cross-site attacks. In this paper, we propose GENPass, a multi-source deep learning model for generating “general” password. GENPass learns from several datasets and ensures the output wordlist can maintain high accuracy for different datasets using adversarial generation. The password generator of GENPass is PCFG+LSTM (PL). We are the first to combine a neural network with PCFG. Compared with Long short-term memory (LSTM), PL increases the matching rate by 16%-30% in cross-site tests when learning from a single dataset. GENPass uses several PL models to learn datasets and generate passwords. The results demonstrate that the matching rate of GENPass is 20% higher than by simply mixing datasets in the cross-site test. Furthermore, we propose GENPass with probability (GENPass-pro), the updated version of GENPass, which can further increase the matching rate of GENPass.

Many chatbots have been developed that provide a multitude of services through a wide range of methods. A chatbot is a brand-new conversational agent in the highspeed changing technology world. With the advance of Artificial Intelligence and machine learning, chatbots are becoming more and more popular. A chatbot is the extension of human interface mediums such as the phone and social platforms. Similarly, Cryptocurrency is a new extension of digital or virtual currency designed to work as a medium of exchange. In the current digital exchanging world, investors and interested parties are eager to know more information about, and the capabilites of, this new type of currency. One of the potential paths to retrieve the info automatically and quickly is through a chatbot. We explored the open source python library, Chatterbot, to apply Itchat API (a WeChat interface) with the aim of building a robot chatting application, I&C Chat, on the topic of cryptocurrency. First, we collected question and answer pairs datasets from Quora websites. Furthermore, we also created API calls to query the real time quote for the top 25 cryptocurrencies. Then we used the collected data to train our chatbot and implemented a logic adapter to receive the price quote of cryptocurrencies based on the incoming question. The Itchat API method will return the best matched answer to the asking party automatically. The response time of different questions has been investigated. The results imply that this application is quite useful, feasible and beneficial to the digital currency world.

Deep neural networks are vulnerable to adversarial examples which are inputs modified with unnoticeable but malicious perturbations.Most defending methods only focus on tuning the DNN itself, but we propose a novel defending method which modifies the input data to detect the adversarial examples.We establish a detection framework based on normalizing filters that can partially erase those perturbations by smoothing the input image or depth reduction work.The framework gives the decision by comparing the classification results of original input and multiple normalized inputs.Using several combinations of gaussian blur filter, median blur filter and depth reduction filter, the evaluation results reaches a high detection rate and achieves partial restoration work of adversarial examples in MNIST dataset.The whole detection framework is a low-cost highly extensible strategy in DNN defending works.

Tracking moving objects is always a critical challenge in cyber-physical systems. Researchers have proposed many tracking algorithms. However, most of the proposed algorithms cannot be used for on-demand deployment because of the unavailable preset fingerprints (prior landmark or context information) in their assumption. Another issue is that those algorithms with models built in an interference-free environment cannot work in interference-rich environments. To address those issues, we propose a localizing and tracking algorithm called Enhanced Field Division (EFD), which dynamically divides the field into areas with unique signatures and tracks the target without any fingerprints. We also implemented a proof-of-concept localization platform to demonstrate the tracking accuracy and the algorithm performance in practical, interference-rich environments.

This study presents a new method for detecting Shrew DDoS (Distributed Denial of Service) attacks and analyzes the characteristics of the Shrew DDoS attack. Shrew DDoS is periodic to be suitable for the server’s TCP (Transmission Control Protocol) timer. It has lower maximum to bypass peak detection. This periodicity makes it distinguishable from normal data packets. By proposing the CCID (Cross-Correlation Identity Distinction) method to distinguish the flow properties, it quantifies the difference between a normal flow and an attack flow. Simultaneously, we calculated the cross-correlation between the attack flow and the normal flow in three different situations. The server can use its own TCP flow timer to construct a periodic attack flow. The cross-correlation between Gaussian white noise and simulated attack flow is less than 0.3. The cross-correlation between single-door function and simulated attack flow is 0.28. The cross-correlation between actual attack flow and simulated attack flow is more than 0.8. This shows that we can quantitatively distinguish the attack effects of different signals. By testing 4 million data, we can prove that it has a certain effect in practice.

Password has become today's dominant method of authentication in social network. While the brute-force attack methods, such as HashCat and John the Ripper, are unpractical, the research then switches to the password guess. The state-of-the-art approaches, such as Markov Model and probabilistic context-free grammars(PCFG), are all based on statistical probability. These approaches have a low matching rate. The methods on neural network have been proved more accurate and practical for password guessing than traditional methods. However, a raw neural network model is not qualified for cross-sites attack since each data set has its own features. This paper proposes a general deep learning model for password guessing, called GENPass. GENPass can learn features from several data sets and ensure the output wordlist high accuracy in different data sets by using adversarial generation. The password generator of GENPass is PCFG+LSTM(PL), where LSTM is a kind of Recurrent Neural Network. We combine neural network with PCFG because we found people were used to set their passwords with meaningful strings. Compared with LSTM, PL increased the matching rate by 16%-30% in the cross-sites tests when learning from a single dataset. GENPass uses several PL models to learn datasets and generate passwords. The result shows that the matching rate of GENPass is 20% higher than that of simply mixing those datasets in the cross-sites test.

Web service is one of the key communications software services for the Internet. Web phishing is one of many security threats to web services on the Internet. Web phishing aims to steal private information, such as usernames, passwords, and credit card details, by way of impersonating a legitimate entity. It will lead to information disclosure and property damage. This paper mainly focuses on applying a deep learning framework to detect phishing websites. This paper first designs two types of features for web phishing: original features and interaction features. A detection model based on Deep Belief Networks (DBN) is then presented. The test using real IP flows from ISP (Internet Service Provider) shows that the detecting model based on DBN can achieve an approximately 90% true positive rate and 0.6% false positive rate.

Energy internet is now an industry hot spot which enables the interconnection and sharing of energy just like the Internet. Inspired by the concept of energy internet, this paper will focus on a designed energy distribution network, using city bus lines running Electric Vehicles (EV) to achieve electric power storage and transmission. This network is made of renewable energy sources providing power, charge stations for power exchange and bus lines as delivery, electric buses serving as the carriers of flowing power in network. This paper will mainly discuss and solve the problem of placing charge stations on city bus map to compose the network framework. Our work includes two optimization algorithms using some ideas of graph theory, simulating with real-world transporting data of different city maps and analyzing the results to evaluate efficiency as well as advantages and disadvantages on algorithms and data sets.

Cloud computing is a new paradigm that provides computing, storage and networking services to end users. Data distribution for cloud computing is different from that in traditional content distribution networks in that it has a direct implication on efficiency of using cloud resources. In this paper we propose a new block-based data distribution mechanism for cloud computing. Instead of using the whole file as a unit of delivery, we divide a file into blocks to improve the efficiency. We design a scheduling algorithm to manage the delivery of blocks to all receivers. The novel aspect of the algorithm is that it can achieve constant distribution time no matter how many receivers need to get the file. We further analyze the overhead caused by dividing a file into blocks. The performance results show that our mechanism can significantly reduce the distribution time, compared with the traditional method and a non-block-based method.

Localization proves to be a promising application of wireless sensor networks. Although a considerable number of algorithms have been designed for low-overhead and high-accuracy localization, problems remain to be tackled such as the way to use anchor-deploying. In this paper, we present a mechanism for range-free localization called Enhanced Map Segmentation (EMS) to deploy and segment the map where precise indoor localization is required. Despite the limits of environmental noise, sensing irregularity, received signal strength (RSS) variation and other unavoidable factors, EMS can be reliable by improving the quality of map segmentation. This paper will present and analyze the enhancing method by a series of simulations. In addition, to deal with ambiguous context positions that confounds the localization, this paper ameliorates the segmentation with context conception mentioned in [1] by statistical methods. In fact, a well-organized deployment and a context-based decision mechanism can make such a layer of abstraction more reliable and compatible.

Wireless sensor networks are often deployed for tracking moving objects. Many tracking algorithms have been proposed with two general assumptions: the preset fingerprints(prior landmark or context information) and an interference-free environment. These algorithms, however, cannot be used for on-demand deployment where finger-prints are unavailable and would perform poorly in interference-rich environments. In this paper, we present a fingerprint-free localizing and tracking algorithm, called Enhanced Field Division (EFD). The EFD algorithm is used to dynamically divide the field into areas with unique signatures and tracks the target, without any finger-prints. We also implemented a proof-of-concept localization platform to demonstrate the tracking accuracy and the algorithm performance in practical, interference rich environment.

Abstract As a new networking paradigm, opportunistic networking communications have great vision in animal migration tracking, mobile social networking, network communications in remote areas and intelligent transportation, and so on. Opportunistic networks are one of the evolutionary mobile ad hoc networks, whose communication links often suffer from frequent disruption and long communication delays. Therefore, many opportunistic forwarding protocols present major security issues, and the design of opportunistic networks faces serious challenges such as how to effectively protect data confidentiality and integrity and how to ensure routing security, privacy, cooperation, and trust management. In this paper, we first systematically describe the security threats and requirements in opportunistic networks; then propose a general security architecture of opportunistic networks; and then make an in‐depth analysis on authentication and access control, secure routing, privacy protection, trust management, and incentive cooperation mechanisms; and at the same time, we present a comparison of various security and trust solutions for opportunistic networks. Finally, we conclude and give future research directions. Copyright © 2014 John Wiley & Sons, Ltd.

Advanced Metering Infrastructure (AMI) is the core component in a smart grid that exhibits a highly complex network configuration. AMI shares information about consumption, outages, and electricity rates reliably and efficiently by bidirectional communication between smart meters and utilities. However, the numerous smart meters being connected through mesh networks open new opportunities for attackers to interfere with communications and compromise utilities assets or steal customers private information. In this paper, we present a new DoS attack, called puppet attack, which can result in denial of service in AMI network. The intruder can select any normal node as a puppet node and send attack packets to this puppet node. When the puppet node receives these attack packets, this node will be controlled by the attacker and flood more packets so as to exhaust the network communication bandwidth and node energy. Simulation results show that puppet attack is a serious and packet deliver rate goes down to 20%-10%.

Vehicle-to-Grid (V2G) is that the energy stored in the batteries of electric vehicles can be utilized to send back to the power grid. And then, the energy in the batteries of electric vehicles can move with electric vehicles (EVs). Based on above characteristics, this paper introduces the concept of a mobile electrical grid and discusses the energy routing problem. It focuses on the optimization problem of how to find routes from the energy sources to charge stations, especially, when some paths are clogged by traffic jam. A bipartite graph model is used to analyze the route problem and two algorithms are presented to compute minimal energy metric route. Both of algorithms are tested by real-world transporting data in Manhattan and the Pioneer Valley Transit Authority(PVTA). Simulations show that the method is efficient.

Energy constraint is a critical hurdle hindering the practical deployment of long-term wireless sensor network applications. Turning off (that is, duty cycling) sensors could reduce energy consumption, however, this would occur at the cost of low sensing fidelity due to sensing gaps introduced. Existing techniques focus mainly on scheduling a network with static anchors. Few methods provides a rigorous approach to confining sensing errors within desirable bounds while seeking to optimize the tradeoff between energy consumption and accuracy of predictions. In this work, we propose a sensing scheduling scheme, called MAS, to support mobile anchors in sensor networks. Within a node, we use a sensing probability bound to control tolerable sensing errors. While communicating with the mobile anchor, nodes trigger additional sensing activities to accommodate the QoS requirement in mobile communication. We validated the concept by constructing a lab-grade mobile anchor that fully supports 4G-LTE communications for monitoring applications. We further conducted simulations to investigate system performance. The simulation results demonstrated that the MAS achieved enhancement performance compared to several other sensing schemes.

Wireless sensor networks (WSNs) are vulnerable to security attacks due to the broadcast nature of transmission and limited computation capability. After intrusion detection systems (IDSs) identifies an mobile intruder, IDS may broadcast the blacklist to all nodes in network. This method is energy inefficient because all nodes have to receive and forward the alarm packet so as to exhaust communication bandwidth and node energy, especially when there are a large number of sensor nodes in the network. This paper develops an energy efficient intrusion prevention mechanism in WSNs called green firewall. It can isolate an intruder with less overhead, and track the intruder to continually prevent the attack. The paper analyzes the overhead cost of the green firewall and compare it with the flooding broadcast method. Extensive analysis and simulations show that green firewall can prevent the attack and effectively reduce redundant alarm packet transmissions which results in less energy consumption.

Vehicle-to-grid provides a viable approach that feeds the battery energy stored in electric vehicles (EVs) back to the power grid. Meanwhile, since EVs are mobile, the energy in EVs can be easily transported from one place to another. Based on these two observations, we introduce a novel concept called EV energy network for energy transmission and distribution using EVs. We present a concrete example to illustrate the usage of an EV energy network, and then study the optimization problem of how to deploy energy routers in an EV energy network. We prove that the problem is NP-hard and develop a greedy heuristic solution. Simulations using real-world data shows that our method is efficient.

Black and gray hole attack is one kind of routing disturbing attacks and can bring great damage to the network. As a result, an efficient algorithm to detect black and gray attack is important. This paper demonstrates an adaptive approach to detecting black and gray hole attacks in ad hoc network based on a cross layer design. In network layer, we proposed a path-based method to overhear the next hop's action. This scheme does not send out extra control packets and saves the system resources of the detecting node. In MAC layer, a collision rate reporting system is established to estimate dynamic detecting threshold so as to lower the false positive rate under high network overload. We choose DSR protocol to test our algorithm and ns-2 as our simulation tool. Our experiment result verifies our theory: the average detection rate is above 90% and the false positive rate is below 10%. Moreover, the adaptive threshold strategy contributes to decreasing the false positive rate.

Efficient implementation of FIR (finite-impulse-response) equalizers for wireless channels has attracted much attention these days as equalizations can improve the link performance in hostile mobile radio environment by compensating for inter-symbol interference created by multipath within time dispersive channels. Meanwhile, the MIMO (multiple-input-multiple-output) technique becomes a trend in wireless channel design. On the other hand, we have observed a Least-Squares rational function system identification algorithm, which approximates FIR impulse response with IIR (infinite-impulse-response) structures effectively. In this paper, we exploit the approximation algorithm and extend the algorithm for a MIMO response approximation which can generate a hardware-efficient MIMO IIR structure for FIR type time domain equalizers in the wireless system. We demonstrate the efficiency and accuracy of our method with MIMO modeling examples.

Much work has been done to modify the TCP protocol to improve TCP performance - mainly throughput, over wireless link. However, such improvements need to change the TCP implementation on user systems, which is usually difficult to deploy. In this paper, we investigate the effectiveness of enhancing the TCP performance over wireless link by tuning the packet scheduling schemes. Based on an opportunistic packet scheduling algorithm we proposed, we demonstrate that TCP throughput can be significantly improved by using channel-aware packet scheduling algorithms without changing the TCP implementation. Our work suggests that in cases where it is impractical to change the TCP protocols, packet scheduling is an effective way to improve the TCP performance over wireless links.

In this paper, we propose a new group key management scheme for wireless sensor networks in terms of the unreliable wireless channel and unsafe environment. Our proposed scheme implements node revocation through a broadcast polynomial to counteract the node compromise attack and inherits the idea of loss tolerance in LiSP to provide a reliable communication. The analysis shows that the proposed scheme can efficiently revoke the compromised sensor nodes, implicitly authenticate the updated group keys and tolerate the key-update message loss under the unreliable wireless communication channel.