Link Attacks Research Articles

Robust synchronization of Markovian jump complex dynamical networks under link attacks: Delay-dependent dynamic event-triggered control approach

This paper investigates the synchronization problem for Markovian jump complex dynamical networks (MJCDNs) subject to link attacks. An extended dynamic event-triggered (DET) control is proposed that takes into account limited network resources and time-varying delay constraints. The proposed method enhances the robustness and resource-saving ability of the DET mechanism, and avoids the Zeno phenomenon. It provides MJCDNs to have a buffering ability for external interference. Additionally, this paper introduces a robust model-based strategy to address scenarios where network attackers inject false data into the link channels between nodes. This approach reduces the complexity of processing MJCDNs subject to link attacks. By leveraging network topology and Lyapunov functions, the criteria are derived to achieve robust synchronization of MJCDNs under link attacks. Finally, simulation examples including Chua’s circuit are presented to verify the validity and superiority of the results.

Secure distributed estimation via an average diffusion LMS and average likelihood ratio test

Secure distributed estimation algorithms are designed to protect against a spectrum of attacks by exploring different attack models and implementing strategies to enhance the resilience of the algorithm. These models encompass diverse scenarios such as measurement sensor attacks and communication link attacks, which have been extensively investigated in existing literature. This paper, however, focuses on a specific type of attack: the multiplicative sensor attack model. To counter this, the paper introduces the Average diffusion least mean square (ADLMS) algorithm as a viable solution. Furthermore, the paper introduces the Average Likelihood Ratio Test (ALRT) detector, which provides a straightforward detection criterion. In the presence of communication link attacks, the paper considers the manipulation attack model and presents an ALRT adversary detector. The analysis extends to these ALRT detectors, encompassing the calculation of adversary detection probability and false alarm probability, both achieved in closed form. The paper also provides the mean convergence analysis of the proposed ADLMS algorithm. Simulation results reveal that the proposed algorithms exhibit enhanced performance compared to the DLMS algorithm, while the incremental complexity remains only marginally higher than that of the DLMS algorithm.

Secure pinning synchronization on aperiodic intermittent event-triggered control in discrete-time complex networks against multi-pattern link attacks

This paper investigates the problem of secure synchronization in aperiodic intermittent event-triggered pinning control for discrete-time complex networks (DCNs) against multi-pattern link attacks. Firstly, in order to reduce communication burden and control cost, a novel aperiodic intermittent event-triggered control (AIEC) with discontinuous characteristics is designed based on periodic sampling, where triggering instants are determined by the intermittent control (IC). Secondly, multiple pattern attack are modeled, as they can interrupt different edges and change the network topology. In addition, multi-pattern attacks for each link are independent and their impact on the coupling topology is analyzed. Thirdly, under destroyed network topology, this paper designs aperiodic intermittent event-triggered pinning control (AIEPC) by combining pinning control (PC) with the AIEC. Meanwhile, based on PC, the isolated node and pinned nodes form a like-directed spanning tree with an asymmetrical coupling matrix, where rooted at the isolated node and only directed connections between the isolated node and pinned nodes. Fourthly, using the segmentation analysis method and the inequality iteration technique, a sufficient condition for exponential synchronization of error system is obtained by considering the instants neighboring different intermittent control and attack intervals. Finally, a simulation example on Chua’s circuit network is provided to verify the validity of the theoretical results achieved in this paper.

Resilience quantification and recovery strategy simulation for urban underground logistics systems under node and link attacks: A case study of Nanjing city

Urban Underground Logistics Systems (UULS) have become an emerging solution to mitigate urban surface traffic congestion, environmental pollution, and surface transport safety risks. However, during the operation of UULS, the use of advanced technologies such as the Internet of Things (IoT) introduces cybersecurity risks to the system. Moreover, severe natural disasters can also cause damage to underground transportation network links. Existing research and planning primarily concentrate on the system design and benefits of UULS, neglecting the system's service level under attack scenarios. This study outlines three representative UULS network prototypes and proposes a resilience quantification method centered on logistics efficiency. It also focuses on comparing the effectiveness of three recovery strategies. These strategies give priority to maximum flow, betweenness centrality, and regional importance, as well as the priority of node and link repairs. The resilience quantification method and recovery strategies are applied in a case study set in Nanjing City. The case study results reveal that the Two-echelon network shows exceptional resilience. Regarding the effectiveness of recovery strategies, the strategy based on maximum flow proves to be the most effective, and focusing on node repair can lead to higher system resilience. Based on these findings, this study offers recommendations to transportation and logistics management decision-makers, focusing on UULS resilience and recovery strategy selection. These recommendations are intended to provide valuable guidance for the planning and design of future UULS, ensuring their resilience and reliability.

MOMTA-HN: A Secure and Reliable Multi-Objective Optimized Multipath Transmission Algorithm for Heterogeneous Networks

With the rapid development of heterogeneous network technologies, such as mobile edge computing, satellite communications, self-organizing networks, and the wired Internet, satisfying users’ increasingly diversified and complex communication needs in dynamic and evolving network environments has become a critical research topic. Ensuring secure and reliable information transmission is essential for stable network operation in these complex environments. Addressing this challenge, this study proposed a secure and reliable multi-objective optimized multipath transmission algorithm for heterogeneous networks to enhance security and reliability during data transmission. The core principle of this algorithm was that multipath transmission can provide additional protection through redundant paths. This redundancy ensured that even if one path is attacked or fails, alternative paths can maintain data integrity and reachability. In this study, we employed the Optimized Non-dominated Sorting Genetic Algorithm II (ONSGA-II) to determine the range of the initial population and filter suitable paths by optimizing them according to different demand objectives. In the path selection process, we introduced an innovative deletion graph method, which ensures that redundant paths do not share any common links with the original paths, except when there are unique links. This approach enhances the independence of transmission paths and improves the security of the transmission process. It effectively protects against security threats such as single points of failure and link attacks. We have verified the effectiveness of the algorithm through a series of experiments, and the proposed algorithm can provide decision-makers with high-reliability and low-latency transmission paths in heterogeneous network environments. At the same time, we verified the performance of the algorithm when encountering attacks, which is superior to other classical algorithms. Even in the face of network failures and attacks, it can maintain a high level of data integrity and security.

Completely Distributed Secure Consensus for Multiagent Systems With a General Directed Graph Under Interaction Link Attacks

Completely Distributed Secure Consensus for Multiagent Systems With a General Directed Graph Under Interaction Link Attacks

Observer-based secure synchronization control of directed complex-valued dynamical networks under link attacks

Observer-based secure synchronization control of directed complex-valued dynamical networks under link attacks

Transforming data‐intensive workflows: A cutting‐edge multi‐layer security and quality aware security framework

SummaryThe data‐intensive workflow application executes tasks on edge servers and cloud platforms in a heterogeneous big‐data computing environment. Cloud and edge servers are vulnerable to node attacks and malicious links due to their wireless connections. Thus, detecting and mitigating rogue nodes in edge server communication environments during workflow execution is crucial. In today's workflow execution landscape, there is a rising emphasis on resolving Quality of Service (QoS) and security concerns within homogeneous execution settings. Workflow execution on diverse computing platforms has received limited research. We are developing an edge‐cloud‐specific Multi‐Layer Security and Quality‐Aware (MLSQA) framework to solve research issues in this area. Node attacks are detected via reputation‐based security features in the MLSQA framework, whereas link attacks are detected using machine learning. A unique trade‐off metrics technique optimizes workflow security and QoS parameters, reducing energy usage and makespan. In addition, a fault‐tolerant, energy‐efficient job offloading technique is described in detail to improve the system's resilience to errors. The experiment is conducted using complex (i.e., memory and CPU intensive) scientific procedures with intermediate job dependencies, namely Inspiral workflow. The MLSQA framework excels in threat detection, demonstrating lower false alarms, reduced energy consumption, and quicker implementation than the recent secure workflow execution architecture.

Attack on communication links analysis and detection over the clustered networks

This research focuses on the subject of attack analysis, detection, and mitigation in a network of MASs with linear dynamics. This network is divided into multiple clusters that are disconnected from one another. A leader is an agent that exists in each cluster and has the ability to interact with other leaders through a strongly connected directed graph at discrete instances. Firstly, we analyze the effect of communication link attacks on the consensus algorithm. We show that the consensus control law may be disturbed by a single attack on a communication channel using principles from matrix theory and graph theory. Then, a bank of impulsive unknown input observers is established to ensure each agent is able to detect the communication link attack. Moreover, a modification of the consensus algorithm of MASs based on an estimated attack is also given. Finally, the results of our research are illustrated using simulations.

Edge-based adaptive secure consensus for nonlinear multiagent systems with communication link attacks

Fully distributed secure consensus control of nonlinear multiagent systems under communication link attacks is achieved, where the design criteria are independent of the Laplacian matrix related to the communication topology and its corresponding eigenvalues. Firstly, this article proposes an edge-based fully distributed adaptive protocol for leaderless nonlinear multiagent systems with communication link attacks to achieve secure consensus control, where the interaction strengths between neighboring agents are adaptively adjusted on the foundation of the transmitted relative states to counteract influences of communication link attacks. Then, the problem of secure consensus control for leaderless nonlinear multiagent systems with communication link attacks is transformed into the stability of secure consensus error systems, and sufficient conditions for leaderless nonlinear multiagent systems achieving fully distributed secure consensus control are given. Furthermore, main results for leaderless nonlinear multiagent systems are extended to leader-following cases. Finally, theoretical results are verified by two numerical examples.

Fully distributed secure consensualization for leaderless nonlinear multiagent systems with communication link attacks

AbstractThe secure consensualization for leaderless nonlinear multiagent systems with communication link attacks is realized in a fully distributed manner in a certain sense that the gain matrix design do not depend on the communication topology. A new virtual reference state estimator is proposed for each agent to estimate the common tracking state, which can adaptively adjust coupling strengths to counteract the influence of communication link attacks. Then, by the Lipschitz condition and the special structure property of the time‐varying nonsingular transformation matrix, the influence of the Lipschitz nonlinearity on secure consensus is linearized, and a design approach of the secure consensus control gain is given on the foundation of the linear matrix inequality. Finally, a numerical example is provided to demonstrate the effectiveness of theoretical results.

Secure synchronization against link attacks in complex networks with event-triggered coupling

This paper concentrates on the secure synchronization problem against link attacks in complex networks with event-triggered coupling. The link attacks for each link are independent, which means that there are multiple attack patterns for coupling communication topology. Then, the relevant model of link attacks with multiple patterns is constructed to analyze the effect on synchronization error system. Furthermore, to reduce communication burden and save coupling resources, a sampled-data-based event-triggered coupling mechanism is established. In this situation, the triggering condition is supervised only at discrete sampling instants. By employing Lyapunov theory and some inequality techniques, sufficient conditions are given to guarantee the synchronization against link attacks with multiple patterns. At last, a simulation example on Chua’s circuit network is provided to demonstrate the effectiveness of the proposed results.

Edge-Based Decentralized Adaptive Pinning Synchronization of Complex Networks Under Link Attacks.

This article studies the pinning synchronization problem with edge-based decentralized adaptive schemes under link attacks. The link attacks considered here are a class of malicious attacks to break links between neighboring nodes in complex networks. In such an insecure network environment, two kinds of edge-based decentralized adaptive update strategies (synchronous and asynchronous) on coupling strengths and gains are designed to realize the security synchronization of complex networks. Moreover, by virtue of the edge pinning technique, the corresponding secure synchronization problem is considered under the case where only a small fraction of coupling strengths and gains is updated. These designed adaptive strategies do not require any global information, and therefore, the obtained results in this article are developed in a fully decentralized framework. Finally, a numerical example is provided to verify the availability of the achieved theoretical outcomes.

Towards Enhancing the Robustness of Scale-Free IoT Networks by an Intelligent Rewiring Mechanism.

The enhancement of Robustness (R) has gained significant importance in Scale-Free Networks (SFNs) over the past few years. SFNs are resilient to Random Attacks (RAs). However, these networks are prone to Malicious Attacks (MAs). This study aims to construct a robust network against MAs. An Intelligent Rewiring (INTR) mechanism is proposed to optimize the network R against MAs. In this mechanism, edge rewiring is performed between the high and low degree nodes to make a robust network. The Closeness Centrality (CC) measure is utilized to determine the central nodes in the network. Based on the measure, MAs are performed on nodes to damage the network. Therefore, the connections of the neighboring nodes in the network are greatly affected by removing the central nodes. To analyze the network connectivity against the removal of nodes, the performance of CC is found to be more efficient in terms of computational time as compared to Betweenness Centrality (BC) and Eigenvector Centrality (EC). In addition, the Recalculated High Degree based Link Attacks (RHDLA) and the High Degree based Link Attacks (HDLA) are performed to affect the network connectivity. Using the local information of SFN, these attacks damage the vital portion of the network. The INTR outperforms Simulated Annealing (SA) and ROSE in terms of R by 17.8% and 10.7%, respectively. During the rewiring mechanism, the distribution of nodes’ degrees remains constant.

A Conditional Privacy-Preserving Certificateless Aggregate Signature Scheme in the Standard Model for VANETs

Vehicular ad hoc networks (VANETs) are the communication foundation for future intelligent transportation systems and guarantee safe driving of intelligent networked vehicles. Moreover, VANETs face a series of security challenges related to the protection of vehicle privacy, authenticity of transmitted information, and bandwidth limitations. To resolve these contradictions, many certificateless aggregate signature (CLAS) schemes have been proposed. However, the majority of them can neither resist malicious-but-passive key generation center attacks, replay attacks, and link attacks, nor track the actual identities of malicious vehicles. Meanwhile, the security of previous CLAS schemes in VANETs is only formally provided in the random oracle model (ROM), which might be insecure in actual implementation. In addition, most CLAS schemes still have problems of large verification delays and high communication overhead. To address the above-mentioned problems, a new conditional privacy-preserving CLAS scheme in VANETs is proposed, which adopts full aggregation technology to reduce computation and bandwidth resources. According to the formal security proofs under the computational Diffie-Hellman problem (CDHP) given in the standard model (SM), the security level of this scheme is higher than that of other CLAS schemes under ROM in practical applications. Additionally, the use of pseudonym mechanism realizes conditional privacy protection in VANETs. The performance analysis shows that this scheme has a higher efficiency in terms of computation and communication overhead compared with several previous CLAS schemes.

K‐anonymity based location privacy protection method for location‐based services in Internet of Thing

AbstractThe wide application of Internet of Things technology has promoted the application of location‐based services (LBS). Users can enjoy various conveniences brought by LBS. However, mobile users should submit their location information to LBS which may lead to privacy disclosure. Therefore, users need to protect their privacy while enjoying the service. If the privacy protection problem cannot be solved, the development of mobile internet business will be greatly affected. The existing location privacy protection methods take little account of real‐time road conditions and are unable to resist link attacks. In this article, a k‐anonymous location privacy protection method based on Voronoi map was proposed. First, the Voronoi diagram was divided according to road network structure and real‐time road network data, and Voronoi unit was established with the intersection as the base point. Then, the base point is sent as an anchor instead of the user's actual location to the third‐party server. The Voronoi diagram generated by the k‐anonymity constituted an anonymous space to resist multi‐query attacks and protected the location. k‐1 user location points that are communicating were selected to form k‐anonymous set with the current user, and send the anonymous set to LBS. Theoretical analysis and experimental results show that the proposed method not only efficiently protected location privacy, but also provided high‐quality service.

Robustness evaluation for real traffic network from complex network perspective

Robustness is a critical characteristic of ubiquitous real networked systems which are often very vulnerable to the random failures or targeted attacks. In this work, we aim to evaluate the robustness of a traffic network which is constructed from real trajectory data. First, we divide a city into grids which are considered as nodes and a link is established if there is at least one taxi dispatch order from one grid to another. Second, we propose two categories of attack strategies: node attacks (e.g. random node failure, and degree, in-degree, out-degree, closeness, clustering coefficient, PageRank-based targeted attacks) and link attacks (e.g. random link failure and link weight, link degree, link betweenness-based targeted attacks). Third, we suppose all elements (i.e. node or link) are simultaneously or iteratively removed, and evaluate the network robustness by employing the relative size of the giant components, average clustering coefficient and average shortest path length. The results show that the degree-based targeted attacks are the most destructive among all attacks, and no matter node or link, the betweenness-based attack is the most effective method to be beneficial to the target index. Moreover, most methods with recalculations take priority over the ones without recalculations.

The responsiveness of criminal networks to intentional attacks: Disrupting darknet drug trade.

Physical, technological, and social networks are often at risk of intentional attack. Despite the wide-spanning importance of network vulnerability, very little is known about how criminal networks respond to attacks or whether intentional attacks affect criminal activity in the long-run. To assess criminal network responsiveness, we designed an empirically-grounded agent-based simulation using population-level network data on 16,847 illicit drug exchanges between 7,295 users of an active darknet drug market and statistical methods for simulation analysis. We consider three attack strategies: targeted attacks that delete structurally integral vertices, weak link attacks that delete large numbers of weakly connected vertices, and signal attacks that saturate the network with noisy signals. Results reveal that, while targeted attacks are effective when conducted at a large-scale, weak link and signal attacks deter more potential drug transactions and buyers when only a small portion of the network is attacked. We also find that intentional attacks affect network behavior. When networks are attacked, actors grow more cautious about forging ties, connecting less frequently and only to trustworthy alters. Operating in tandem, these two processes undermine long-term network robustness and increase network vulnerability to future attacks.

Modeling and analysis of cascading node-link failures in multi-sink wireless sensor networks

Due to the prominent advantages in network lifetime and energy balance, the application of multi-sink wireless sensor networks (WSNs) is becoming more and more widespread. However, their cascading robustness is still rarely studied. Therefore, in this paper, a realistic cascading model for multi-sink WSNs is proposed. In this model, two load metrics are proposed to characterize the load distributions of sensor nodes and wireless links, and the cascading process of the network is jointly promoted by node overload events and link overload events, which can better reflect the cascading characteristics of multi-sink WSNs in practical scenarios. In addition, we focus on the cascading robustness of the network in the face of node attacks and link attacks. Through extensive experiments, we found that there are critical thresholds for both node capacity and link capacity, which can determine whether capacity expansion is helpful; there is a critical threshold for network load distribution, which can determine whether cascading failures occur; node attacks are more likely to trigger cascading failures than link attacks; increasing node capacity can more effectively reduce the damage of cascading failures to the network. The discovery of the above results can provide theoretical guidance for users to build a more robust multi-sink WSN against cascading failures.

Cascading failures in wireless sensor networks with load redistribution of links and nodes

Existing cascading models for wireless sensor networks (WSNs) cannot correctly reflect the traffic feature of WSNs. In this work, we build a more practical cascading model for WSNs, in which the network load is defined according to two new traffic metrics “sink-oriented node betweenness” and “sink-oriented link betweenness” and the cascading process is jointly propelled by the load redistribution of sensor nodes and wireless links. In addition, load-redistribution schemes are designed according to the principle of “idle capacity”. Simulation results show that the network invulnerability is positively related to the tolerance coefficient and negatively related to the exponential coefficient. The minimum costs needed to resist intentional node attacks are more expensive than the costs needed when facing intentional link attacks.