The landscape of cyberattacks has transcended from mere hobbyist pursuits of cybercriminals to a lucrative business model, facilitating their sustenance. Concurrently, the cybercrime market has evolved into a complex ecosystem. Empowered by this environment, cybercriminal tactics have evolved from simple, isolated activities to intricate and coordinated cyberattacks. In this article, we reveal a new type of cyberattack paradigm termed Attack Exploiting Cyber-arms Industry (AECI), which, despite its potential for severe impact, requires less investment and entails fewer obstacles and risks compared to traditional methods. However, this type of attack is still neglected by security researchers and communities and this is the first work focusing on this type of attack. To elucidate AECI, we provide an overview of the cyber-arms industry and introduces an attack model. The model dissects each phase of AECI to illuminate its operational mechanics and strategic imperatives. Furthermore, to assess its potential impact, a mathematical model is proposed to estimate the scale of infection attributable to AECI. Through analysis of a specific attack case, our findings demonstrate that AECI can generate significant impacts within a brief timeframe, akin to the magnitude observed with the Mirai botnet. The proposed model is demonstrated to prove instrumental in effectively analyzing AECI and providing accurate estimations of its infection scale.

Virtual resource allocation, widely referred to as Virtual Network Embedding (VNE), has received increasing attention from both industry and academia. In fact, VNE has ubiquitously become a technological leap in Internet of Vehicles (IoV) which is a fundamental framework for the anticipated success of future intelligent transportation. The general VNE problem has been shown to be NP-hard [1], [2] and finding an optimal VNE in a dynamic environment like IoV is even more challenging. In fact, research on VNE in dynamic environments, where connected moving vehicles act as substrate nodes to provision requested services, is still in its nascent stages. As a result, this paper proposes a Genetic Algorithm (GA) assisted by a novel fitness function considering critical network topological attributes and resource constraints for dealing with the online VNE problem considering vehicle mobility. Simulation results based on the Random Waypoint (RWP) mobility model indicate that the proposed algorithm achieves better performance compared to several existing VNE algorithms.

The content security policy (CSP) is a World Wide Web Consortium (W3C) standard, designed to prevent and mitigate security vulnerabilities, such as cross-site scripting (XSS) attacks, data injection attacks, and clickjacking attacks on websites. In this article, we present a newly discovered front-end Web attack that uses the current object storage services vulnerability of cloud vendors to bypass CSP. We selected the object storage services from two cloud vendors with the most users, i.e., Google and Amazon, to conduct systematic and large-scale research and analysis. Three cyberspace search engines are used to retrieve data, from which we analyze the consequence and damage range of this security breach. We focus on reporting four key aspects of this security breach: 1) how to use object storage services to bypass CSP; 2) analysis on the existence of such vulnerability in real-world websites; 3) analysis on the existing security vulnerabilities in current object storage services; and 4) the new strategy on object storage services that we propose to use to eliminate the discovered security threat.

Autonomous driving is a core application that greatly benefits from Internet of Vehicles (IoV). The calculation of the precise positions of Connected Autonomous Vehicles (CAVs) is mainly done using a Deep Neural Network (DNN) which requires significant computing power. Therefore, reducing the computational overhead and improving the efficiency are urgent problems to be solved. In this paper, we first propose a CAV cooperative learning architecture based on blockchain to improve the positioning accuracy of vehicles. Then, we introduce an error precision sharing model between CAVs. The proposed framework enables CAVs to train vehicle positioning accuracy models locally and exchange them via a blockchain network. Such a distributed training architecture further reduces the computing power required. Extensive simulation results show that the proposed scheme can also significantly improve the accuracy of the trajectory error compared to existing approaches.

We introduce FLightNER, a Federated Learning (FL) model that extends an existing state-of-the-art Named-Entity Recognition (NER) model using prompt-tuning known as LightNER. FLightNER allows the aggregation of only the trainable parameters of LightNER without model accuracy degradation saving 10 GB per client enabling more clients to join a federation without extending the central server’s memory. We evaluate our approach against two baselines using three diverse datasets with different distributions across up to seven clients in a federation. We empirically show that compared to the centrally-trained LightNER model, FLightNER outperforms it by 19% when performed on a medical dataset with label imbalance across clients and matches it when performed on two balanced datasets: CoNLL and I2B2. Furthermore, we use and evaluate two well-established memory-saving techniques: AdaFactor optimizer and Automatic Mixed Precision on our FL approach. Our findings enable owners of sensitive data such as healthcare practitioners to efficiently train an NER model collaboratively, with low memory requirements, while keeping their data on-premise.

Recently, Internet of Vehicles (IoV) and Machine Learning (ML) have attracted more and more attention. Considering inefficient real-time training and high requirements on computing capabilities of centralized data collection, performing Distributed Machine Learning (DML) in IoV has become an important research branch. However, the heterogeneity, mobility, and distrust among IoV nodes affect how to execute DML effectively, securely, and in a salable manner. In this paper, a blockchain-based Cooperative Learning framework combined with a Deep Compression method (CLDC) is proposed. First, we improve the local training efficiency of lightweight IoV nodes by using deep compression method. Meanwhile, we have introduced a blockchain system in CLDC, the significance of which is that we have completed the transformation from centralized architecture to distributed framework through the blockchain, and shared local training results in a verifiable manner. The framework uses non-tamperable features of the blockchain to ensure the security of local training results. Moreover, we propose a Learning-based Redundant Byzantine Fault Tolerance (L-RBFT) protocol, in which the primary node needs to confirm the loss percentage of learning in the transaction before forwarding the RBFT messages. The significance of L-RBFT is to ensure that IoV nodes obtain the best training results through the consensus of blockchain nodes. We use it to solve the computing and communication resource allocation problem in IoV to clarify the operating mechanism of the proposed framework. The experimental results prove that this scheme performs better when compared with the traditional centralized deep reinforcement learning method.

As a basic component in multimedia applications, object detectors are generally trained on a fixed set of classes that are pre-defined. However, new object classes often emerge after the models are trained in practice. Modern object detectors based on Convolutional Neural Networks (CNN) suffer from catastrophic forgetting when fine-tuning on new classes without the original training data. Therefore, it is critical to improve the incremental learning capability on object detection. In this article, we propose a novel Residual-Distillation-based Incremental learning method on Object Detection (RD-IOD). Our approach rests on the creation of a triple-network based on Faster R-CNN. To enable continuous learning from new classes, we use the original model as well as a residual model to guide the learning of the incremental model on new classes while maintaining the previous learned knowledge. To better maintain the discrimination between the features of old and new classes, the residual model is jointly trained with the incremental model on new classes in the incremental learning procedure. In addition, a two-level distillation scheme is designed to guide the training process, which consists of (1) a general distillation for imitating the original model in feature space along with a residual distillation on the features in both image level and instance level, and (2) a joint classification distillation on the output layers. To well preserve the learned knowledge, we design a 2-threshold training strategy to guide the learning of a Region Proposal Network and a detection head. Extensive experiments conducted on VOC2007 and COCO demonstrate that the proposed method can effectively learn to incrementally detect objects of new classes, and the problem of catastrophic forgetting is mitigated. Our code is available at https://github.com/yangdb/RD-IOD.

Privacy is currently one of the most concerned issues in Cyberspace. Tor is the most widely used system in the world for anonymously accessing Internet. However, Tor is known to be vulnerable to end-to-end traffic correlation attacks when an adversary is able to monitor traffic at both communication endpoints. In this paper, we present a set of novel Trapper Attacks that can be used to deanonymize user activities by both AS-level adversaries and Node-level adversaries in a Tor network. First, AS-level adversaries can exploit the occasional failures of censored network to selectively control entry guards of the Tor users. Second, the adversaries can exploit poor reliability of the Tor communication (e.g., natural churn) to compromise the exiting nodes and the anonymous path. Once the adversaries gain control of the routes, they can identify and inspect any traffic entering and leaving the Tor network, consequently, deanonymize a Tor user’s activity in the network. To demonstrate the effectiveness and feasibility of this attacks, we implemented a tool that can launch the proposed Trapper Attacks to automatic reveal communication relationships between a Tor user and its destinations running on a live Tor network. We also present a formal analysis framework to evaluate the integrity of the Tor network. With this framework, we successfully obtained quantitative estimates of Tor’s security vulnerability. The proposed Trapper Attacks are also designed to scale up in real-world Tor networks. Namely, it allows an adversary to perform deanonymization in honey relays effectively, and compromise the anonymity of Tor clients in real time. Our experimental results show that the proposed attacks succeed in less than 40 seconds achieving a 100% accuracy rate and a false positive rate close to 0.

Internet of Things (IoT) has become one of the fastest-growing technologies and has been broadly applied in various fields. IoT networks contain millions of devices with the capability of interacting with each other and providing functionalities that were never available to us before. These IoT networks are designed to provide friendly and intelligent operations through big data analysis of information generated or collected from an abundance of devices in real time. However, the diversity of IoT devices makes the IoT networks’ environments more complex and more vulnerable to various web attacks compared to traditional computer networks. In this article, we propose a novel ensemble deep learning based web attack detection system (EDL-WADS) to alleviate the serious issues that IoT networks faces. Specifically, we have designed three deep learning models to first detect web attacks separately. We then use an ensemble classifier to make the final decision according to the results obtained from the three deep learning models. In order to evaluate the proposed WADS, we have performed experiments on a public dataset as well as a real-word dataset running in a distributed environment. Experimental results show that the proposed system can detect web attacks accurately with low false positive and negative rates.

As one of the most pervasive current modes of communication, email needs to be fast and reliable. However, spammers and attackers use it as a primary channel to conduct illegal activities. Although many approaches have been developed and evaluated for spam detection, they do not provide sufficient accuracy. This deficiency results in significant economic losses for organizations. In this article, we first propose a framework for creating novel spam filters using Keras to combine a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) classification models. We then use this framework to introduce a specific solution applicable to realistic scenarios involving dynamic incoming email data in real-time. This solution takes the form of a real-time content-based spam classifier. We evaluate its performance concerning accuracy, precision, recall, false-positive, and false-negative rates. Our experimental results show that our approach can significantly outperform existing solutions for real-time spam detection.

Edge computing provides high-class intelligent services and computing capabilities at the edge of the networks. The aim is to ease the backhaul impacts and offer an improved user experience. However, the edge artificial intelligence exacerbates the security of the cloud computing environment due to the dissociation of data, access control, and service stages. In order to prevent users from carrying out lateral movement attacks in an edge-cloud computing environment, in this paper we propose a real-time lateral movement detection method, named CloudSEC, based on an evidence reasoning network for the edge-cloud environment. First, the concept of vulnerability correlation is introduced. Based on the vulnerability knowledge and environmental information of the network system, the evidence reasoning network is constructed, and the lateral movement reasoning ability provided by the evidence reasoning network is then used. The experiment results show that CloudSEC provides a strong guarantee for the rapid and effective evidence investigation, as well as real-time attack detection.

Hospitals and health care organizations collect large amounts of detailed health care data that is in high demand by researchers. Thus, the possessors of such data are in need of methods that allow for this data to be released without compromising the confidentiality of the individuals to whom it pertains. As the geographic aspect of this data is becoming increasingly relevant for research being conducted, it is important for an anonymization process to pay due attention to the geographic attributes of such data. In this paper, a novel system for health care data anonymization is presented. At the core of the system is the aggregation of an initial regionalization guided by the use of a Voronoi diagram. We conduct a comparison with another location-based system of anonymization, GeoLeader. We show that our system is capable of producing results of a comparable quality with a much faster running time.

In high-performance computing (HPC), workflow-based workloads are usually data intensive for exploratory analysis of a scientific computation problem that may involve a large parameter space. To achieve the best performance, storage resource constraint is always a pragmatic concern in reality as the potential problem space scale, especially in big data science, as well as its required dataset are ever growing to outpace any increasing rate of storage capacity. Therefore, the workflow computation in a HPC environment with finite storage resources is still a practical topic that is worthwhile studying. To this end, we propose a novel scheduling framework that enhances the scheduling policies of Versioned Name Space and Overwrite-Safe Concurrency, introduced in our earlier work, with abilities to handle the deadlock problem in workflow computation with finite storage constraints. We achieve this goal by leveraging the data dependency information of the workflow to integrate a collection of deadlock resolution algorithms into the workflow scheduler. With such integration, after extensive simulation-based studies we conclude that the enhanced scheduling policies can solve the deadlock problem introduced by the storage constraints caused by big data overflow. More interestingly, we demonstrate that our enhanced scheduling policies perform better than the cases where only pure deadlock algorithms are applied when storage is highly constrained in terms of makespan performance.

In this paper, we study the problem of co-migrating a set of service replicas residing on one or more redundant virtual servers in clouds in order to satisfy a sequence of mobile batch-request demands in a cost effective way. With such a migration, we can not only reduce the service access latency for end users but also minimize the network costs for service providers. The co-migration can be achieved at the cost of bulk-data transfer and increases the overall monetary costs for the service providers. To gain the benefits of service migration while minimizing the overall costs, we propose a co-migration algorithmMigkfor multiple servers, each hosting a service replicas.Migkis a randomized algorithm with a competitive cost of$O(\frac{\gamma\, \log \,n}{\min \lbrace \frac{1}{\kappa },\frac{\mu }{\lambda \,+\,\mu }\rbrace })$to migrate$\kappa$services in a static$n$-node network where$\gamma$is the maximal ratio of the migration costs between any pair of neighbor nodes in the network, and where$\lambda$and$\mu$represent the maximum wired transmission cost and the wireless link cost respectively. For comparison, we also study this problem in its static off-line form by proposing a parallel dynamic programming (hereafter DP) based algorithm that integrates the branch&bound strategy with sampling techniques in order to approximate the optimal DP results. We validate the advantage of the proposed algorithms via extensive simulation studies using various requests patterns and cloud network topologies. Our simulation results show that the proposed algorithms can effectively adapt to mobile access patterns to satisfy the service request sequences in a cost-effective way.

When deploying a wireless sensor network in an unknown environment, commonly referred to as Region of Interest (ROI), the main goal is for the entire region to be covered by the sensing ranges of the deployed sensors. While this goal of full coverage is easily achieved in presence of human intervention, it becomes problematic if the region is dangerous or inaccessible to human. An approach recently proposed to solve the problem is to use a robot to deploy the sensors; the main advantages respect to the alternative of employing mobile sensors are the reduced costs (due to manufacture and maintenance cost of common static sensors vs. mobile ones) and the reduced complexity of the coordination and control algorithms. Indeed several solution algorithms to achieve deployment of sensors by a robot in an unknown region have been proposed in the literature. Unfortunately, even when restricted to orthogonal regions (e.g., city maps, building plans, etc), all the existing algorithms fail to achieve full coverage of the ROI. Specifically, following the existing protocols, the robot would leave uncovered areas near either the boundaries or critical areas (e.g. areas that are linked to the rest of the region by a narrow corridor). In this paper we present an algorithm that overcomes these problems and guarantees that the deployment of the sensors by the robot achieves full coverage in any simply connected orthogonal ROI, whose topology is unknown to the robot. The proposed algorithm has minimal requirements: it does not need GPS but only local orientation by the robot; the communication range of a deployed sensor is limited to its deployed neighbours, and the robot has a similar range; the total number of sensors used is minimal. Also minimal are the robot's memory requirements, the total amount of robots movements and of communication between robot and sensors.

Providing transactional primitives of NAND flash based solid state disks (SSDs) have demonstrated a great potential for high performance transaction processing and relieving software complexity. Similar with software solutions like write-ahead logging (WAL) and shadow paging, transactional SSD has two parts of overhead which include: 1) write overhead under normal condition, and 2) recovery overhead after power failures. Prior transactional SSD designs utilize out-of-band (OOB) area in flash pages to store transaction information to reduce the first part of overhead. However, they are required to scan a large part of or even whole SSD after power failures to abort unfinished transactions. Another limitation of prior approaches is the unicity of transactional primitive they provided. In this paper, we propose a new transactional SSD design named Möbius. Möbius provides different types of transactional primitives to support static and dynamic transactions separately. Möbius flash translation layer (mFTL), which combines normal FTL with transaction processing by storing mapping and transaction information together in a physical flash page as atom inode. By amortizing the cost of transaction processing with FTL persistence, MFTL achieve high performance in normal condition and does not increase write amplification ratio. After power failures, Möbius can leverage atom inode to eliminate unnecessary scanning and recover quickly. We implemented a prototype of Möbius and compare it with other state-of-art transactional SSD designs. Experimental results show that Möbius can at most 67% outperform in transaction throughput (TPS) and 29 times outperform in recovery time while still have similar or even better write amphfication ratio comparing with prior hardware approaches.

In this paper, we consider task-level scheduling algorithms with respect to budget and deadline constraints for a batch of MapReduce jobs on a set of provisioned heterogeneous (virtual) machines in cloud platforms. The heterogeneity is manifested in the popular “pay-as-you-go” charging model where the service machines with different performance would have different service rates. We organize the batch of jobs as a k-stage workflow and study two related optimization problems, depending on whether the constraints are on monetary budget or on scheduling length of the workflow. First, given a total monetary budget B, by combining an in-stage local greedy algorithm (whose optimality is also proven) and dynamic programming (DP) techniques, we propose a global optimal scheduling algorithm to achieve minimum scheduling length of the workflow within O(kB2). Although the optimal algorithm is efficient when B is polynomially bounded by the number of tasks in the MapReduce jobs, the quadratic time complexity is still high. To improve the efficiency, we further develop two greedy algorithms, called Global Greedy Budget (GGB) and Gradual Refinement (GR), each adopting different greedy strategies. In GGB we extend the idea of the local greedy algorithm to the efficient global distribution of the budget with minimum scheduling length as a goal whilst in GR we iteratively apply the DP algorithm to the distribution of exponentially reduced budget so that the solutions are gradually refined. Second, we consider the optimization problem of minimizing cost when the (time) deadline of the computation D is fixed. We convert this problem into the standard Multiple-Choice Knapsack Problem via a parallel transformation. Our empirical studies verify the proposed optimal algorithms and show the efficiencies of the greedy algorithms in cost-effectiveness to distribute the budget for performance optimizations of the MapReduce workflows.

Using service migration in Clouds to satisfy a sequence of mobile batch-request demands is a popular solution to enhanced QoS and cost effectiveness. As the origins of the mobile accesses are frequently changed over time, moving services closer to client locations not only reduces the service access latency but also minimizes the network cost for service providers. However, these benefits do not come without compromise. The migration comes at cost of bulk-data transfer and service disruption, as a result, increasing the overall service costs. In this paper, we study the problem of dynamically migrating a service in Clouds to satisfy a sequence of mobile batch-request demands in a cost effective way. More specifically, to gain the benefits of service migration while minimizing the increased monetary costs, we propose a search-based dynamic migration algorithm that can effectively migrate a single or multiple servers to adapt to the changes of access patterns with minimum service costs. The algorithm is characterized by effective uses of historical access information to conduct virtual moves of a set of servers as a whole under a certain condition so as to overcome the limitations of local search in cost reduction.

Unlike in the consumer electronics and personal computing areas, in the HPC environment hard disks can hardly be replaced by SSDs. The reasons include hard disk's large capacity, very low price, and decent peak throughput. However, when latency dominates the I/O performance (e.g., when accessing random data), the hard disk's performance can be compromised. If the issue of high latency could be effectively solved, the HPC community would enjoy a large, affordable and fast storage without having to replace disks completely with expensive SSDs. In this paper, we propose an almost latency-free hard-disk dominated storage system called LiU for HPC. The key technique is leveraging limited amount of SSD storage for its low-latency access, and changing data layout in a hybrid storage hierarchy with low-latency SSD at the top and high-latency hard disk at the bottom. If a segment of data would be randomly accessed, we lift its top part (the head) up in the hierarchy to the SSD and leave the remaining part (the body) untouched on the disk. As a result, the latency of accessing this whole segment can be removed because access latency of the body can be hidden by the access time of the head on the SSD. Combined with the effect of prefetching a large segment, LiU (Lift it Up) can effectively remove disk access latency so disk's high peak throughput can now be fully exploited for data-intensive HPC applications. We have implemented a prototype of LiU in the PVFS parallel file system and evaluated it with representative MPI-IO micro benchmarks, including mpi-io-test, mpi-tile-io, and ior-mpi-io, and one macro-benchmark BTIO. Our experimental results show that LiU can effectively improve the I/O performance for HPC applications, with the throughput improvement ratio up to 5.8. Furthermore, LiU can bring much more benefits to sequential-I/O MPI applications when the applications are interfered by other workloads. For example, LiU improves the I/O throughput of mpi-io-test, which is under interference, by 1.1-3.4 times, while improving the same workload without interference by 15%.

A quality-driven approach to software development and testing demands that, ultimately, the requirements of stakeholders be validated against the actual behavior of an implementation under test (IUT). In Model-Based Testing, much work has been done on the generation of functional test cases. But few approaches tackle the executability of such test cases. And those that do, offer a solution in which tests and test cases are not directly traceable back to the actual behavior of an IUT. Furthermore, very few approaches tackle non-functional requirements. Consequently, we have implemented a validation framework that does support the modeling and automated validation of a set of functional and non-functional requirements against several candidates IUTs. We report here on the key characteristics of this prototype and briefly discuss lessons learnt from its use in the context of a graduate course.

In ad hoc networks and wireless sensor networks, several routing algorithms rely on the knowledge by the network nodes of their own geographic location and those of others. For cases where a node doesn't have its own positioning device (e.g., GPS), Alfaro et al. propose several algorithms that a node can run to determine its geographic position using position reports from neighbors. In this paper, we first present the evil ring attack, an attack on the geographic location algorithms of Alfaro et al. that misleads nodes about the true position of their neighbors. An attacker sends false reports with a position that sits on a circle centered at the victim's location and of a radius equal to the distance between the victim and attacker. The attack succeeds because the calculation of the distance between the victim and attacker is not affected despite this fake position. We then present and analyze an evil ring attack detection algorithm in which a position-unaware sensor node crosschecks the consistency of the information it collects from its neighbors with the information collected by other trusted neighbors. This algorithm detects the existence of neighbors running the evil ring attack. We propose a general distributed algorithm for a) localizing sensors in a wireless sensor network in the presence of some malfunctioning ones, and b) detecting such malfunctioning sensors.

A black hole is a highly harmful host that disposes of visiting agents upon their arrival without any observable trace of the destruction. The problem of locating the black hole in asynchronous ring network is known to be solvable by a team of mobile agents if each node is equipped with a whiteboard. A simpler and less expensive inter-communication and synchronization mechanism is provided by tokens: each agent has available a bounded number of tokens that can be carried, placed in a node or/and on a port of the node, or removed. All tokens are identical and no other form of communication or coordination is available to the agents. It is known that locating the black hole in an anonymous ring network using tokens is feasible when the team of agents is initially collocated (i.e. they all start from the same host). Recently, the more difficult case when the agents are scattered (i.e., when the agents do not start from the same host) has also been examined and solutions requiring only O(1) tokens per agent but using a total of O(n2) moves have been presented. The number of moves can be reduced to O(kn + n log n) if the number k of agents is known. In this paper, we study the impact of orientation and knowledge of team size on the cost of black hole location by scattered agents with tokens. We prove that, in oriented rings, the number of moves can be reduced from O(n2) to the optimal Theta(nlogn) using only O(1) tokens per agent, without any knowledge of the team size. This result holds even if both agents and nodes are anonymous. Interestingly, the proposed algorithm solves, with the same cost, also the leader election problem and the rendezvous problem for the scattered agents despite the presence of a BH.

Summary form only given. We present an executable model for a family of algorithms dealing with leader election in a ring topology. We follow the traditional approach of system family engineering. That is, we develop a feature model that captures variability across these algorithms. We then proceed to produce a generator. This generator receives as inputs specific values for each of the variation points (i.e., features) we identify. And it produces the behavior corresponding to the specific configuration of features at hand. Contrary to existing generative programming literature, we do not resort to C++ meta-programming but instead develop an executable model using Rational Rose RT. More precisely, we have designed a single state chart that can model all the algorithms of the family we studied. We focus here on how to obtain such a state chart, rather than on the identification of the features we used, or on ROSE-RT semantics. We do believe however that our approach can be reused to provide a semantically unified and executable modelling approach for other families of algorithms.