The deceptive nature of false data injection (FDI) attacks and the hysteresis effect of asynchronous leakage delay (ALD) can weaken the resilient load frequency control (RLFC) system’s dynamic response and stability. Therefore, this paper investigates the system dynamic fluctuations caused by the coupling of FDI and ALD. First, a dynamic attack model is established. An attack modulator (AM) is introduced to dynamically adjust the attack process to accurately simulate attacks on critical control paths. Second, we systematically characterize the asynchronous leakage delay caused by multi-source communication link switching. This delay behavior is embedded into the control system. This enables the system to more comprehensively reflect feedback delay characteristics under complex communication environments. Next, a robust control method is designed to enhance the system’s response to FDI and ALD under these complex situations, which can effectively ensure the stability of the system. Then, the improved Lyapunov-Krasovskii functional is used to analyze the system performance. Finally, simulations of a dual-region power system are conducted. The simulation results verify the effectiveness of the proposed method in terms of disturbance immunity, control stability, and engineering adaptability.

This paper investigates the differential privacy consensus problem for a class of multiagent systems under dynamic topologies. To meet the requirements of power consumption, a random communication strategy is proposed in which each agent sends data to its neighbors with different probabilities. For analyzing the effect of time-varying topology and coupling strength among agents on system performance, a necessary and sufficient condition for almost sure convergence of differential privacy consensus systems is established. Furthermore, the convergence rate and convergence accuracy of the system are also studied. By formulating the communication costs and topological characteristics as a constrained problem, a convex optimization algorithm for fast convergence of the differential privacy consensus system is proposed. In addition, the differential privacy of the agents is analyzed, and the optimal noise parameters that achieve a trade-off between convergence accuracy and privacy levels are derived. A numerical simulation is presented to demonstrate the effectiveness of the developed approach.

In the battlefield of cyberspace, sophisticated attackers often operate by following meticulously designed cyber kill chains, enabling them to maintain a persistent presence within victim systems while evading conventional detection mechanisms. Traditional honeypot-based deception defenses aim to uncover such threats by luring attackers into exposing their malicious activities through decoy systems. However, advanced attackers are frequently able to identify and avoid these traps, making it increasingly challenging to detect and engage them effectively. To overcome this challenge, this study proposes a novel defensive paradigm named as the safeguard mode, which emphasizes the covert identification of attackers rather than solely preventing initial breaches. By proactively recognizing potential threats in a hidden manner, victim systems can be better protected through early threat intelligence. Based on the propsoed safeguard mode concept, we propose$Honey^{4}$, abbreviated as$H^{4}$, a comprehensive framework designed to systematically entrap advanced threats.$H^{4}$comprises four core components: Honeypoint, Honeyproxy, Honeytrace, and Honeycenter, which work in concert to deceive, monitor, and analyze attacker behavior. Furthermore, we explore how Artificial Intelligence Generated Content (AIGC) techniques can enhance$H^{4}$'s capabilities, particularly as attackers themselves begin to leverage AI-driven tactics. The practical efficacy of the proposed safeguard mode and the$H^{4}$framework has been validated through being deployed in real scenarios including the 19th Asian Games and the Canton Fairs, and$H^{4}$has successfully captured a significant number of threatening IP addresses and malicious behavioral patterns, generating actionable cyber threat intelligence that fundamentally safeguards system defense.

Semantic attacks against programmable logic controllers (PLCs) can manipulate industrial control systems (ICS) and cause severe physical disruptions. Yet, existing researches remain target-agnostic: they fail to determine which PLC tags or physical process are most critical when exploited. To address this gap, we introduce Pipe4Stux, a semantic attack framework that generates effective PLC attacks by leveraging key tags and attack semantics. Pipe4Stux first constructs a lexical analyzer and a data dependency subgraph from PLC control logic to capture the relationships among variables and operations. It then identifies key tags, derives semantic features of attacks, and formulates process-aware attack tactic. Experimental results on real ICS scenarios demonstrate that Pipe4Stux enables destructive and reproducible semantic attacks, revealing the potential impact of compromising key tags during specific physical states. In addition, Pipe4Stux provides the first systematic and objective methodology for semantic attack synthesis, offering practical guidance for defense. The framework can also support continuous monitoring by designating key tags as alarm variables within supervisory control and data acquisition (SCADA) systems such as WINCC.

This paper investigates the security issues of average consensus networks under false data injection attacks. Based on the intention of the adversary, the attacks to be analyzed are categorized into two types. The first type aims to rapidly diverge the states of the agents to infinity, while the second type seeks to mislead the states towards the adversary's desired values. A necessary and sufficient condition for Type-I attacks to bypass the anomaly detector is first provided, along with an algorithm for generating false data sequences. Subsequently, for Type-II attacks, the closed-form expression of the optimal attack is derived using dynamic programming. To address these vulnerabilities, a watermarking-based data transmission strategy is proposed by resorting to cryptographic pseudo-random sequences. The influence of watermarking parameters on detection performance across different attack scenarios is analyzed. It is demonstrated that the proposed strategy can effectively assist the anomaly detector in identifying stealthy attacks or mitigate its impact on the consensus networks by adjusting the watermarking parameters. A numerical example is provided to demonstrate the validity of the developed results.

This paper investigates the resilience of autonomous vehicle (AV) systems against denial-of-service (DoS) attacks in networked environments. While existing fuzzy logic and event-triggered control strategies have been explored, they often fail to address the combined impacts of DoS-induced network congestion, data compression errors, and the lack of real-time adaptive learning to maintain stability. To address this gap, this work introduces a unified, resilient fuzzy logic control framework. First, a Performance Error Estimation (PEE) framework is developed to quantify control performance degradation under cyber threats, informing the design of the resilient controller. Second, an efficient data compression scheme is designed to mitigate DoS-induced network overload, ensuring reliable real-time data transmission under constrained bandwidth. Third, an Intelligent Event-Triggered Fuzzy Controller (IETFC) is proposed, which adaptively optimizes its triggering threshold via a mini-batch machine learning algorithm, effectively balancing communication efficiency with system robustness. Validation on the CarSim–Simulink platform demonstrates that the proposed framework successfully mitigates DoS-induced performance degradation, enhances triggering sparsity, and improves computational efficiency. By embedding learning-based adaptation into fuzzy control, this work provides a scalable and secure solution for AV systems operating under adversarial conditions.

Network scanning is crucial for both network management and cybersecurity. However, due to the vast address space of IPv6, brute-force scanning is infeasible. Seed-based target generation algorithms have recently attracted considerable research attention. However, existing target generation algorithms lack a deeper exploration of patterns, leading to poor capture of dense regions and consequently low hitrate. To address this issue, we propose 6Global, a dynamic IPv6 active address scanning method assisted by global perspective. 6Global first performs rapid clustering of seed addresses based on their descriptive attributes. Then, for each cluster, patterns are generated in a bottom-up manner based on entropy, using subranges to represent patterns and resulting in denser patterns. Finally, dynamic scanning is conducted using these patterns. During scanning, the reward of each pattern is dynamically adjusted based on its active density and global statistics, which enhances the capability in capturing dense regions. Experimental results on six seed datasets show that 6Global overall outperforms seven baseline methods and demonstrates significant advantages across multiple datasets.

This article investigates reliable leader-follower synchronization of linear multi-agent systems (MASs) under composite communication faults. Firstly, a composite double-Bernoulli link model is proposed to characterize inter-layer and intra layer communications. It captures communication mismatch by allowing independent Bernoulli link states with distinct stochastic reliabilities across the two interaction layers. Secondly, a passive predictor-reset mechanism is embedded into a modified distributed measurement to address packet losses. This mechanism efficiently leverages locally predicted surrogate values to enable uninterrupted distributed feedback computation and resets these surrogates upon data reception. Furthermore, a reliable solo adaptive protocol is developed, where each follower updates only one scalar adaptive gain together with a reliability-oriented baseline parameter. In addition, a Lyapunov-Krasovskii func tional (LKF) with an inverse gain weighting is constructed to establish the mean-square ultimate boundedness of the closed loop synchronization error. This result provides an explicit reliability guarantee under composite link faults, including low probability link activations and packet losses. Finally, numerical simulations demonstrate the effectiveness and resilience of the proposed scheme.

With the widespread adoption of network encryption, traditional Deep Packet Inspection (DPI) has become ineffective. Existing approaches that exploit side-channel information often suffer from limited representational capacity, poor robustness, and heavy reliance on large labeled datasets. To address these limitations, we present FGCL, a robust framework for malicious encrypted traffic detection based on graph contrastive learning. FGCL models each bidirectional flow as a Flow Graph (FG), capturing fine-grained interaction patterns between communicating entities. At its core is a novel two-stage augmentation strategy: at the traffic level, realistic obfuscation tactics are simulated to improve robustness, while at the graph level, structural transformations are applied to learn invariant representations. This enables the graph encoder to be pre-trained on large-scale unlabeled data, producing highly generalizable embeddings that can be effectively fine-tuned with only a few labeled samples. Extensive experiments on three real-world datasets show that FGCL consistently outperforms state-of-the-art methods in few-shot learning, adversarial robustness, and overall detection accuracy, achieving up to a 10% F1-score improvement in detecting obfuscated traffic. These results highlight FGCL as an effective and practical solution for encrypted traffic detection in scenarios characterized by label scarcity and adversarial conditions.

This study examines denial-of-service (DoS) attacks on networked control systems (NCSs) caused by TCP/IP protocol vulnerabilities. It conducts a comprehensive analysis of hacker tactics to uncover vulnerability exploitation techniques. The research reformulates the performance error estimation (PEE) problem, framing it as a search for ellipsoid constraints within a P-dependent set. It introduces the Higher-Order Weight Method (HOWM) to optimize sampling intervals, leveraging the unique properties of zero-order-hold sampling. The study enhances Lyapunov-Krasovskii functions (LKFs) using HOWM by partitioning integral terms and applying quadratic scaling to optimize control algorithms, thereby reducing conservatism and tightening upper-bound criteria. Additionally, it proposes an innovative Integral Event-Triggered Control (IETC) strategy for estimating system performance errors. The performance of the proposed control algorithm is rigorously evaluated through simulations on a complex 2-degree of freedom (DoF) helicopter system (HS). Note to Practitioners—This study delves into DoS attacks targeting NCSs due to TCP/IP protocol vulnerabilities. It conducts an exhaustive analysis of hacker tactics to uncover techniques used to exploit these vulnerabilities. The research reframes the PEE problem, casting it as a search for ellipsoid constraints within a P-dependent ellipsoidal set. It introduces the HOWM to optimize sampling intervals, leveraging the unique characteristics of zero-order-hold sampling. Additionally, the study enhances LKFs using HOWM by partitioning integral terms and applying quadratic scaling to optimize the control algorithm, thereby reducing conservatism and tightening upper-bound criteria. Moreover, it introduces an innovative IETC strategy for estimating system performance errors. The performance of the proposed control algorithm is rigorously evaluated through simulations on a complex 2-DoF HS.

This article addresses the challenge of achieving asymptotic stability in nonlinear networked control systems (NNCSs) amid denial-of-service (DoS) attacks, particularly under constrained communication resources. We begin by establishing a practical DoS attack model using the NSL-KDD dataset, which provides a realistic depiction of DoS attack dynamics based on real-world data. We then introduce the iterative shrinkage-thresholding algorithm (ISTA) to supervise the adaptive event-triggered controller (AETC), ensuring that system parameters are adjusted effectively while conserving communication resources. We develop an enhanced data compression mechanism to further mitigate the impact of DoS attacks on communication servers. Additionally, we construct an asymmetric Lyapunov-Krasovskii function (LKF) to rigorously verify the asymptotic stability of NNCSs. Finally, we empirically validate the effectiveness of our proposed AETC using an autonomous vehicle (AV) model.

This paper addresses the critical challenge of achieving asymptotic stability in networked control systems (NCSs) under denial-of-service (DoS) attacks, focusing on maintaining security and stability within bandwidth-constrained environments. First, we construct a practical attack model using the NSL-KDD dataset to provide a realistic representation of DoS attack dynamics, capturing key attributes such as attack duration and frequency. Then, an iterative shrinkage-thresholding algorithm (ISTA) is introduced to supervise the adaptive sampled-data controller (ADSC), dynamically optimizing the sampling period to enhance control performance while minimizing communication overhead. To further mitigate the impact of DoS attacks, we propose a novel data compression mechanism that adapts to varying network conditions, ensuring efficient bandwidth utilization and preserving critical control data fidelity. In addition, the stability of the NCS is rigorously verified through Lyapunov-Krasovskii functions (LKFs), demonstrating robust system behavior even under adverse network conditions. Finally, the effectiveness and practicality of the proposed approach are validated through experimental studies on a 2-degree-of-freedom (2-DoF) helicopter system, confirming its capability to ensure stability, optimize communication efficiency, and mitigate the effects of DoS attacks in real-world scenarios.

With the rapid development of mobile and wireless technologies, the industrial manufacturing sector has entered the era of automation. The proliferation of mobile devices, sensor networks, and remote monitoring systems enables factory equipment to be more flexibly connected and controlled. However, the trend towards industrial networks also brings new challenges. Industrial control systems (ICSs) and programmable logic controllers (PLCs) are more susceptible to hacker attacks and interference. Honeypoints have been developed to protect ICSs from addressing these threats, including potential internal attacks. Honeypoints are active deception systems that mitigate the limitations of conventional defense mechanisms, which successfully entice and neutralize internal enemies. This paper presents the PLC Honeypoint Communication Generator (PHCG), enhancing honeypoint protective capabilities in industrial IoT systems. Using an automated construction process, PHCG provides a convenient and efficient deployment method, ensuring quick and effective functioning. The functionality of a PLC relies on a data generation model trained on PLC response data. This model allows PHCG to imitate genuine PLC responses accurately when given authorized commands. The experimental results illustrate the adaptability of information produced by PHCG in different communication processes, with satisfactory timescales for both model training and data generation.

PLC, as the core of industrial control systems, has been turned into a focal point of research for attackers targeting industrial control systems. However, current researched methods for attacking PLCs suffer from issues such as lack of precision and limited specificity. This paper proposes a novel attack method called AOIFF. Specially, AOIFF extracts the binary control logic code from a running PLC and reverses the binary code into assemble code. And then awareness of industrial field information is extracted from assemble code. Finally, it is based on awareness that attack code is generated and injected into a PLC, which can disrupt the normal control logic and then launch precise attacks on industrial control systems. Experimental results demonstrate that AOIFF can effectively perceive information in industrial field and initiate precise and targeted attacks on industrial control systems. Additionally, AOIFF achieves excellent results in the reverse engineering of binary code, enabling effective analysis of binary code.

In the scenario of smart healthcare applications, the Internet of Medical Things (IoMT) devices, equipped with limited resources, would offload numerous computation-heavy tasks to an edge server through 5G networks. However, IoMT devices should usually move around different diagnostic areas in smart healthcare systems, leading to the dynamics of the uplink channel quality. Moreover, the burst generation of a substantial number of tasks from IoMT devices can result in congestion within the computing queue of the edge server. And, heterogeneous services in IoMT devices make it hard to collect global information for a central controller to get the optimal optimization for all IoMT devices. So, how to determine task offloading among IoMT devices in a distributed scenario of smart healthcare applications should be considered appropriately and comprehensively. In this paper, we investigate task offloading in mobile edge computing (MEC) through wireless networks. To improve the utilization of wireless resources, non-orthogonal multiple access (NOMA) is adopted in 5G networks. We first formulate the mobility of IoMT devices as a Hidden Markov Model (HMM) and the problem of task offloading policy as a distributed Partial Markov Decision Process (Dec-POMDP). Then, we propose a mobility-aware method based on Multi-agent reinforcement learning for task offloading in 5G NOMA-enabled networks. In our approach, task offloading scheduling for each IoMT device in NOMA-enabled 5G networks is considered to improve energy efficiency and guarantee service quality. Besides, the time complexity and the existence of a Nash equilibrium for our proposed Dec-POMDP method are theoretically derived. Simulations are conducted to show that our algorithm outperforms other alternative methods in energy consumption under the delay constraint.

There is a large amount of real-time data, e.g., measurement data and instructions, among controllers, sensors, and actuators in the Industrial IoT. These data are vulnerable to unauthorized access and tampering. In addition, once the controller is controlled by malicious code, it may send out dangerous instruction that is not compliant with the preset control process, which seriously interferes with the industrial control process. To achieve correct and undisturbed control based on real-time data, we propose an attribute-based access control (ABAC) method for real-time data in the Industrial IoT to mitigate unauthorized access and tampering, and noncompliant operation. First, we analyze the abnormal behaviors of real-time data interaction in the Industrial IoT. Second, we propose the multilevel hash identity authentication method to identify and block unauthorized access and tampering with real-time data. And, then we model the timing relationship and task logic relationship of the control process into the attribute fields of the ABAC method to identify and block noncompliant operations. Further, we design an access control module and display the lightweight deployment under the availability constraints of the control service. Finally, the proposed access control method is analyzed, proved, and experimented with. The results show that the proposed method can prevent unauthorized and noncompliant behaviors to field real-time data, meanwhile, it has a controllable delay and better scalability.

This article presents a comprehensive solution to mitigate network congestion in T-S fuzzy networked control systems caused by denial-of-service (DoS) attacks and quality-of-service (QoS) queuing mechanisms. We develop a novel data compression mechanism to alleviate network congestion and use a mini-batch descent gradient algorithm to optimize trigger thresholds, thereby reducing bandwidth usage. In addition, we introduce asymmetric Lyapunov–Krasovskii functions to decrease the number of decision variables, which improves the reliability and robustness of the control algorithm. Finally, we propose an intelligent event-triggered controller supervised by mini-batch machine learning and validate it on the joint CarSim–Simulink platform. Experimental results demonstrate that our approach reduces the sensitivity of autonomous vehicle systems to network fluctuations while ensuring system stability under network congestion caused by DoS attacks.

This study investigates the stability problem in nonlinear networked control systems (NCSs). First, innovative compression rules are introduced to mitigate network congestion and bandwidth utilization issues stemming from quality of service (QoS) queuing mechanisms and denial of service (DoS) attacks. We develop an intelligent trigger controller supervised by a mini-batch machine learning (MBML) algorithm to optimize network bandwidth utilization. Furthermore, we formulate more generalized Lyapunov-Krasovskii functions (LKFs) to simplify mathematical derivations, and we employ appropriate integral inequalities to minimize constraints. Finally, experimental evaluations are conducted on an autonomous vehicle (AV) using the joint CarSim-Simulink platform to verify the effectiveness of the proposed intelligent trigger controller.

This short paper addresses the challenge of network congestion in T-S fuzzy networked control systems (NCSs) caused by denial of service (DoS) attacks and quality of service (QoS) queuing mechanisms. Firstly, a novel signal compression mechanism is introduced to mitigate network congestion. The trigger threshold is optimized using a mini-batch descent gradient algorithm, effectively reducing bandwidth utilization. Furthermore, tailored Lyapunov-Krasovskii functions (LKFs) are established for the system, and we propose an intelligent event-triggered controller (IETC) under machine learning supervision. Finally, the effectiveness of the proposed approach is demonstrated through rigorous verification on the joint CarSim-Simulink platform.

This article addresses the issue of mismatched communication delay (CD) in dual-channel nonlinear networked control systems (NCSs) resulting from the quality of service (QoS) mechanism’s queue management. The focus is on the importance of ensuring communication security in NCSs, particularly in the presence of asynchronous cyber attacks (ACAs). First, improved Lyapunov–Krasovskii functions (LKFs) are constructed, taking into account sampling signals, CDs, and nonlinearities in the system. Additionally, a novel looped-functional approach is introduced to reduce the initial constraint of the criterion. Then, the control algorithm’s performance is optimized by achieving a tighter upper bound on the integral term and applying quadratic scaling. To ensure stability and security under ACAs, a fuzzy memory sample-data controller (MSAC) is proposed. This controller leverages a machine learning algorithm to address the optimization problem of data sampling period selection, thereby minimizing resource usage and operating efficiently within the system’s limited bandwidth. Finally, numerical simulations are conducted using the dynamic equations of the inverted pendulum system (IPS) to validate the practicality of the proposed theoretical approach.

The burgeoning landscape of blockchain technology has made the security of deployed smart contracts an imperative concern. While existing security measures excel in pre-deployment testing, they fall short in protecting smart contracts once they are deployed, leaving them susceptible to malicious attacks. In this paper, we propose a novel Smart Contract Firewall framework designed to bridge this security gap. Functioning as a dynamic gateway, the framework employs real-time transaction inspection through adaptable filtering rules, enabling the identification and rollback of malicious transactions as they occur. Our empirical analysis demonstrates the framework's efficacy in mitigating a majority of existing vulnerabilities in the deployed smart contracts. Although the added layer of security comes at a cost, we prove that the increased gas expenses could be limited to 30 % -50 % for most transactions. This trade-off, we argue, is a small price to pay for significantly enhanced security.

ICN treats contents as the first citizens and faces serious scalability challenges. Especially for the content provider mobility scenario, the routing updates and routing efficiency should be cost-effective. This paper proposes a hierarchical name-based routing (HNR) for provider mobility. HNR focuses on how to find the mobile provider when given an immutable provider name. Based on the idea that the content provider generally moves within a certain topology area over a period of time, HNR first divides the topology into multiple partition topologies, then it adopts a hierarchical routing scheme that combines two suitable routing schemes for local mobility and global mobility. The experiments by simulation demonstrate that HNR achieves a good tradeoff between efficiency and scalability.

Traditional static taint analysis based on bytecode analysis such as GadgetInspector to detect deserialization vulnerabilities always faced precision problems. For example, missing the fact that taints flowing to members in called methods, type confusion, and chaotic inheritance relationships when detecting deserialization vulnerabilities, which would lead to many error results. To alleviate these problems, this paper considers three measures of improving precision of detecting deserialization vulnerabilities, including cross-function members data flow tracking, local variables and arguments types inference, and call chain subject inference based on inheritance relationships.

Entity alignment aims to match identical entities in different knowledge graphs (KGs). In recent years, entity alignment methods for encyclopedic KGs have achieved significant effectiveness. However, the characteristics of KGs of some specific domains differs from encyclopedic KGs, so that encyclopedic entity alignment methods do not perform well in domain KGs. Vulnerability KGs are a typical type of domain KG characterized by a large number of entities and limited structural variations, but strong heterogeneity across different graphs. The neighborhood matching-based entity alignment methods are effective on vulnerability KGs. However, previous neighborhood matching methods have primarily focused on aligning neighborhoods where both entities and relations are aligned simultaneously, neglecting the neighborhoods where only entities are aligned. Vulnerability KGs typically contain a large number of entities but have a limited types of relations. As a result, each relation often connects a significant number of entities. Incorrect matching of relations can potentially result in incorrect matching of all connected entities, leading to severe error propagation.In this paper, we propose a neighborhood matching based method VNM, for entity alignment in vulnerability KGs. VNM considers two layers of neighborhood, that is, the neighborhood that entities and relations both align and the neighborhood that only entities align. Our method not only mitigates the error propagation caused by incorrect relation matching but also leverages richer neighborhood information. Additionally, inspired by the phenomenon of semantic translation in word embeddings, we introduce a regularizer for semantic embedding of one-to-many and many-to-one relations in vulnerability KGs. Experimental results on four real-world vulnerability datasets demonstrate that our method outperforms existing methods in terms of performance.

In softwarized industrial networking, honeypot identification is very important for both the attacker and the defender. Existing honeypot identification relies on simple features of honeypot. There exist two challenges: The simple feature is easily simulated, which causes inaccurate results, whereas the advanced feature relies on high interactions, which lead to security risks. To cope with these challenges, in this article, we propose a secure fuzzy testing approach for honeypot identification inspired by vulnerability mining. It utilizes error handling to distinguish honeypots and real devices. Specifically, we adopt a novel identification architecture with two steps. First, a multiobject fuzzy testing is proposed. It adopts mutation rules and security rules to generate effective and secure probe packets. Then, these probe packets are used for scanning and identification. Experiments show that the fuzzy testing is effective and corresponding probe packet can acquire more features than other packets. These features are helpful for honeypot identification.

Stemming from the recent evolutionary progress of the 5G wireless communication and Internet of Things (IoT) relevant technologies, the vision of the Internet of Connected Vehicles (IoCV) has become more apparent. On the basis of the state-of-the-art IoCV conceptual implementations, the location of the vehicles is one of the essential driven data of IoCV, and the location privacy issue needs to be taken into account. However, when scrutinizing into IoCV, noticeable challenges of location-aware scenario has raised. For IoCV, location is more than just query criteria like in Location-based Services (LBSs) of mobile Internet. It is also the underpinning data of various types of IoCV underlying mechanisms and functions. This difference makes preserving location privacy in IoCV quite different from the traditional privacy scenarios. In this paper, an overall analysis of end-user location privacy in IoCV was performed. To solve the location privacy dilemma, we proposed an intent prediction-based approach named LocJury, which benefits from the emerging concept of Intent-based Networking (IBN). LocJury provides location privacy by learning and estimate the intent of location access and will penalize those malicious location accesses. By simulating the conceptual IBN-based IoCV application scenario, which relies on the location accesses, the performance of LocJury is evaluated under various circumstances. The simulation result verified the effectiveness of our proposed method.

Crowdsensing systems collect various types of data from sensors embedded on mobile devices owned by individuals. These individuals are commonly referred to as workers that complete tasks published by crowdsensing systems. Because of the relative lack of control over worker identities, crowdsensing systems are susceptible to data poisoning attacks which interfering with data analysis results by injecting fake data conflicting with ground truth. Frameworks like TruthFinder can resolve data conflicts by evaluating the trustworthiness of the data providers. These frameworks somehow make crowdsensing systems more robust since they can limit the impact of dirty data by reducing the value of unreliable workers. However, previous work has shown that TruthFinder may also be affected by the data poisoning attack when the malicious workers have access to global information. In this article, we focus on partially observable data poisoning attacks in crowdsensing systems. We show that even if the malicious workers only have access to local information, they can find effective data poisoning attack strategies to interfere with crowdsensing systems with TruthFinder. First, we formally model the problem of partially observable data poisoning attack against crowdsensing systems. Then, we propose a data poisoning attack method based on deep reinforcement learning, which helps malicious workers jeopardize with TruthFinder while hiding themselves. Based on the method, the malicious workers can learn from their attack attempts and evolve the poisoning strategies continuously. Finally, we conduct experiments on real-life data sets to verify the effectiveness of the proposed method.

Large-scale high-speed URL matching is a key operation in many network security systems and surveillance applications in Wireless Sensor Networks. Classic string matching algorithms are unsuitable for large-scale URL filtering due to speed or memory consumption. This paper proposes an extend Wu-Manber algorithm (XWM) which takes advantage of the encoding characteristics of the URL greatly to improve the matching performance of the algorithm. It first adopts the pattern string window selection method to optimize Wu-Manber’s hash process, and then combines hash tables and associative containers to optimize the string comparison process. The experimental results on actual 10 million patterns show that XWM can achieve speeds that are twice as fast as traditional algorithms, especially when the shortest pattern string length is longer, it is more advantageous.

Due to complexity of the train energy absorbers, multiple parameters must be considered in their structure optimization. However, the traditional optimization design methods lack an overall point of view for structure analysis and, therefore, cannot deal with multi-objective problems comprehensively. In this paper, a cooperative optimization platform is proposed to establish the design and optimization process, using a modular modeling system for the high speed train collision absorber. Playing the role of each module, the platform can have a real-time grasp of the entire optimization design process. In addition, with the help of the powerful file data processing system and driving engine of computer, the whole optimization design process is automated, which improves data analysis and post-processing ability. Therefore, the optimization platform can realize automation of optimization design process, shorten optimization time and reduce analysis costs. This platform can achieve the goal of collaborative optimization.

The burgeoning technology of Mobile Edge Computing (MEC) is attracting the traditional Location-Based Service (LBS) and Location Service (LS) to deploy due to its nature characters such as low latency and location awareness. Although this transplant will avoid the location privacy threat from the central cloud provider, there still exist the privacy concerns in the LS of MEC scenario. Location privacy threat arises during the procedure of the fingerprint localization, and the previous studies on location privacy are ineffective because of the different threat model and information semantic. To address the location privacy in MEC environment, we designed LoPEC, a novel and effective scheme for protecting location privacy for the MEC devices. By the proper model of the Radio Access Network (RAN) access points, we proposed the noise-addition method for the fingerprint data, and successfully induce the attacker from recognizing the real location. Our evaluation proves that LoPEC effectively prevents the attacker from obtaining the user's location precisely in both single-point and trajectory scenarios.

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.

This paper presents Griffin, a scheme of geometric routing on flat names to conduct massive content distribution and retrieval. A tree-based metric space T is proposed according to the concept of hierarchical division of symbol space. In Griffin, the network topology is embedded into the T-space, and content names are mapped to the T-space. Content publication and retrieval are supported by geometric routing in the T-space. Different from previous embedding schemes, Griffin constructs the T-space according to the network topology before embedding. In contrast to prior name resolution schemes, Griffin operates directly on the network topology without establishing an overlay. The correctness of Griffin is proved by the greediness of geometric routing. The experiments by simulation demonstrate that Griffin is efficient and scalable.

This study presents pattern matching algorithms, based on vector and matrix models that are suitable for parallel pattern matching. On these two models, we further proposed the vector-based single-pattern matching (VBSP) and the matrix-based multi-pattern matching (MBMP) algorithms, as well as the matrix-based multi-pattern approximate (MBMPA) algorithm and the matrix-based multi-pattern exact (MBMPE) algorithm. The G-MBMP algorithm refers to the implementation of the MBMP algorithm on a graphics processing unit (GPU). The performance of the G-MBMPA is better than that of the G-impMASM. The performance of the G-MBMPE is better than that of the G-WM (GPU-based WM algorithm) and that of the G-AC algorithms (GPU-based AC algorithm). The memory of the G-MBMPE algorithm is the least of the three algorithms and is significantly less than that of the G-AC algorithm.

A warehouse layout problem under fuzzy random environment is considered, in which different types of materials need to be placed in a warehouse so that the total transportation cost is minimized. For convenience of handling, the materials in the same class need to be placed in the adjacent cells. As a result, we construct expected value model for the problem and then design a hybrid intelligent algorithm for this model. Finally, some numerical examples are presented to show the efficiency of the algorithm.