Random Attack Research Articles

Resilience optimization analysis of smart mining cluster cyber-physical systems based on the NK model

This paper explores the challenges and opportunities presented by the digitization and intelligent clustering in the mining industry, focusing on the security and inter-system influences within Cyber-Physical Systems (CPS) of smart mining clusters. Utilizing the NK model and complex adaptive systems theory, we construct a dual-layered network and cascade failure model, alongside a dynamic repair strategy that adapts to environmental changes. By analyzing the critical parameters of node number (N) and relationship density (K), we assess the resilience of smart mining CPS under cascade failures with dynamic repairs. Our findings reveal that dynamic repair strategies significantly enhance network resilience against both random and deliberate attacks, with N and K acting as crucial regulators of repair effectiveness. This suggests that adapting N and K can improve repair outcomes and system resilience, despite increasing complexity. The paper discusses the practical implications of these strategies through simulation and real-world scenarios, highlighting the need for adaptive repair strategies in enhancing smart mining CPS resilience and process safety. Introducing the NK model to CPS resilience research offers new insights into the resilience of smart mining cyber-physical systems and provides a comprehensive risk analysis method for these systems in complex scenarios.

Discrete event-triggered security control for Markovian CVNNs with additive time-varying delays under random deception attacks

This paper is concerned with the security stabilization problem for a class of Complex-valued Neural Networks (CVNNs) with Markov Jump Parameters (MJPs) and Additive Time-varying Delays (ATVDs) under Random Deception Attacks (RDAs). Different from the existing literature, the instant and strength of RDAs considered in this paper is both random, which is more in line with the real situation. Secondly, a general Lyapunov–Krasovskii Functional (LKF) contains more information about MJPs and ATVDs is constructed, and a new Complex-valued Reciprocally Convex Inequality (CVRCI) containing more free matrices and ATVDs parameters is proposed, which play a key role in reducing the conservativeness of security stabilization criteria. Thirdly, a Discrete Event-triggered Mechanism (DETM) is introduced to mitigate the transmission burden of communication networks, in which the triggering condition of DETM mainly relies on the current sampled state and the last triggered state. Then, by combining with the LKF, CVRCI, DETM, and other analysis techniques, some less conservative security stabilization criteria for the underlying systems are provided in terms of Linear Matrix Inequalities (LMIs). Finally, the effectiveness of our results are verified by two numerical examples and a practical example.

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer’s disease

Abstract Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer’s disease (AD). Computational simulations and animal experiments have hinted at the theory of activity-dependent degeneration as the cause of this hub vulnerability. However, two critical issues remain unresolved. First, past research has not clearly distinguished between two scenarios: hub regions facing a higher risk of connectivity disruption (targeted attack) and all regions having an equal risk (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We refined the hub disruption index to demonstrate a hub disruption pattern in functional connectivity in autosomal dominant AD that aligned with targeted attacks. This hub disruption is detectable even in preclinical stages 12 years before the expected symptom onset and is amplified alongside symptomatic progression. Moreover, hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in amyloid-beta positron emission tomography cortical markers, consistent with the activity-dependent degeneration explanation. Taken together, our findings deepened the understanding of brain network organization in neurodegenerative diseases and could be instrumental in refining diagnostic and targeted therapeutic strategies for AD in the future.

Event-triggered sliding mode control for networked T–S fuzzy delayed systems against random injection attacks

This paper investigates the event-triggered sliding mode control (SMC) problem for networked T–S fuzzy delayed systems subject to random injection attacks, where the event-triggered mechanism (ETM) is introduced to alleviate data congestion and improve network efficiency. Under an open network environment, malicious attackers may randomly inject false information into the controller–actuator channel to compromise the integrity of the data. A new fuzzy sliding mode controller is constructed based on a non-parallel distributed compensation strategy, which introduces the distribution information of the attack probability. Then, the membership function dependent (MFD) analysis technique is adopted to reconstruct the membership functions in order to reduce the conservatism caused by the mismatch between the membership functions of fuzzy model and controller. Furthermore, the co-design conditions for the ETM and the fuzzy sliding mode controller are provided, which can ensure that the resulting closed-loop fuzzy system is mean-square exponentially ultimately bounded (EUB) and the sliding surface is reachable. Finally, a simulation experiment with tunnel diode circuit application is utilized to demonstrate the validity of the proposed SMC scheme.

Random subspace ensemble-based detection of false data injection attacks in automatic generation control systems

Automatic Generation Control (AGC) systems in smart grids are increasingly vulnerable to cyber-attacks, particularly False Data Injection (FDI) attacks, due to their reliance on information and communication technologies. These vulnerabilities pose significant threats to the reliable operation of power systems. To address this challenge, this research paper introduces the machine learning (ML) based cyberattack detection technique designed to identify FDI attacks with the highest accuracy proficiently. The study involves a comprehensive analysis of three features: the original discrete signal feature, the cycle-to-cycle-based feature, and the sample-to-sample-based feature. These features are utilized for detecting FDI attacks and distinguishing them from normal load variations. The research meticulously collects data by simulating diverse FDI attacks, including step attacks, pulse attacks, random attacks, and normal load variation cases on an AGC power system. Four ML classifiers are selected and compared for the classification task. The simulation results reveal that the sample-to-sample-based feature proves highly effective in distinguishing FDI attacks compared to the original signal and cycle-to-cycle-based features. Notably, the results indicate that the random subspace ensemble (RaSE) classifier, utilizing sample-to-sample-based features, effectively identifies and classifies all normal and FDI attacks. This research provides valuable insights into the potential of ML techniques for enhancing FDI attack detection in the AGC of power systems. It provides a potential pathway for overcoming the limitations associated with traditional model-based FDI detection methods.

Targeting attack activity-driven networks.

Real-world complex systems demonstrated temporal features, i.e., the network topology varies with time and should be described as temporal networks since the traditional static networks cannot accurately characterize. To describe the deliberate attack events in the temporal networks, we propose an activity-based targeted attack on the activity-driven network to investigate temporal networks' temporal percolation properties and resilience. Based on the node activity and network mapping framework, the giant component and temporal percolation threshold are solved according to percolation theory and generating function. The theoretical results coincide with the simulation results near the thresholds. We find that targeted attacks can affect the temporal network, while random attacks cannot. As the probability of a highly active node being deleted increases, the temporal percolation threshold increases, and the giant component increases, thus enhancing robustness. When the network's activity distribution is extremely heterogeneous, network robustness decreases consequently. These findings help us to analyze and understand real-world temporal networks.

Synergistic action of novel maltohexaose-forming amylase and branching enzyme improves the enzymatic conversion of starch to specific maltooligosaccharide

As attractive functional ingredients, maltooligosaccharides (MOS) are typically prepared by controlled enzymatic hydrolysis of starch. However, the random attack mode of amylase often leads to discrete product distribution, thereby reducing yields and purities. In this study, a novel glycoside hydrolase family 13 amylase AmyEs from marine myxobacteria Enhygromyxa salina was identified efficient maltohexaose (G6)-forming ability (40 %, w/w). By deciphering external chain length, we found that the high density of α-1,6-branching points benefits the G6 formation of AmyEs with high purity (71–82 %), indicating the substrate selectivity of AmyEs toward high-branched starch. Based on this, asynchronous conversion strategy was designed to enhance specific MOS yield from corn starch by exploiting branching enzymes and AmyEs, and the purity and yield of G6 respectively increased by 9.5 % and 5 % compared to single AmyEs treatment. Our results demonstrate that combinatorial catalysis of MOS-forming amylases and branching enzymes provides a favorable industrial preparation of specific MOS.

Robust Optimization Research of Cyber-Physical Power System Considering Wind Power Uncertainty and Coupled Relationship.

To mitigate the impact of wind power uncertainty and power-communication coupling on the robustness of a new power system, a bi-level mixed-integer robust optimization strategy is proposed. Firstly, a coupled network model is constructed based on complex network theory, taking into account the coupled relationship of energy supply and control dependencies between the power and communication networks. Next, a bi-level mixed-integer robust optimization model is developed to improve power system resilience, incorporating constraints related to the coupling strength, electrical characteristics, and traffic characteristics of the information network. The upper-level model seeks to minimize load shedding by optimizing DC power flow using fuzzy chance constraints, thereby reducing the risk of power imbalances caused by random fluctuations in wind power generation. Furthermore, the deterministic power balance constraints are relaxed into inequality constraints that account for wind power forecasting errors through fuzzy variables. The lower-level model focuses on minimizing traffic load shedding by establishing a topology-function-constrained information network traffic model based on the maximum flow principle in graph theory, thereby improving the efficiency of network flow transmission. Finally, a modified IEEE 39-bus test system with intermittent wind power is used as a case study. Random attack simulations demonstrate that, under the highest link failure rate and wind power penetration, Model 2 outperforms Model 1 by reducing the load loss ratio by 23.6% and improving the node survival ratio by 5.3%.

Enhancing bird habitat networks in metropolitan areas: Resilience assessment and improvement strategies – A case study from Shanghai

Constructing bird habitat networks (BHNs) is crucial for maintaining the health and service equilibrium of urban ecosystems, especially in large metropolitan areas where the pressure of urbanization is intense. However, most existing BHNs fail to account for the dynamic changes and unique requirements of local species, leading to homogenized construction outcomes and ecological corridor objectives. This study employs a comprehensive approach to identify bird habitat patches using multiple high-quality sources, then utilize circuit theory and complex network theory to construct and assess the resilience of BHN. Our key findings showed: (1)93 bird habitat sources were identified, predominantly situated in the continuous green spaces of southern and southeastern Shanghai, whereas habitat sources in the city center and other densely built-up areas are more dispersed, highlighting them as prime targets for future ecological restoration efforts. (2) The distribution of bird habitat corridors exhibits significant spatial heterogeneity, with primary corridors predominantly spanning the southwestern and eastern parts of the study area, while secondary corridors are more abundant in the western and northern parts, forming a denser network, whereas the central area shows fewer and more isolated corridors. (3) The decline in structural and functional resilience was notably more rapid under targeted attacks than under random attacks, underscoring the need to prioritize crucial bird habitat sources on the city's periphery, especially near highly urbanized areas, in urban planning and biodiversity conservation efforts to sustain ecological balance and biodiversity. These insights provide a crucial scientific basis for urban planners, emphasizing the integration of biodiversity conservation into urban development strategies by optimizing ecological sources and corridors to balance development with ecological preservation.

Bipartite secure synchronization criteria for coupled quaternion-valued neural networks with signed graph

This study explores the bipartite secure synchronization problem of coupled quaternion-valued neural networks (QVNNs), in which variable sampled communications and random deception attacks are considered. Firstly, by employing the signed graph theory, the mathematical model of coupled QVNNs with structurally-balanced cooperative–competitive interactions is established. Secondly, by adopting non-decomposition method and constructing a suitable unitary Lyapunov functional, the bipartite secure synchronization (BSS) criteria for coupled QVNNs are obtained in the form of quaternion-valued LMIs. It is essential to mention that the structurally-balanced topology is relatively strong, hence, the coupled QVNNs with structurally-unbalanced graph are further studied. The structurally-unbalanced graph is treated as an interruption of the structurally-balanced graph, the bipartite secure quasi-synchronization (BSQS) criteria for coupled QVNNs with structurally-unbalanced graph are derived. Finally, two simulations are given to illustrate the feasibility of the suggested BSS and BSQS approaches.

Analysis of Agricultural Water Security Based on Network Invulnerability: A Case Study in China's Virtual Water Trade Networks

Abstract“Invulnerability” of complex network was firstly introduced to virtual water (VW) research, aiming to broaden the scope of studies on water use and management. Beginning with the construction of China's virtual water trade networks (VWTNs) of major grain crops, Node Degree (K) and Betweenness Centrality (B) are employed to evaluate and rank the importance of China's 31 regions. Regions with high values for both indicators are identified as playing pivotal roles in the VWTNs: Jiangsu (ranking 1st for both K and B), Hubei (2nd for K, 3rd for B), Henan (3rd for K, 6th for B), Hebei (4th for K, 4th for B), Hunan (4th for K, 5th for B). Using this ranking to simulate the invulnerability of VWTNs under random and intentional attacks. The results reveal a rapid decrease in both Network Efficiency (E) and Maximum Connectivity (C) under intentional attack. In comparison to seven random attacks, E falls below 0.1 and C drops below 0.5 after only three intentional attacks, and the network completely collapsed after 10 intentional attacks. This highlights the VWTN's vulnerability in maintaining food supply and agricultural water security when key regions are subjected to man‐made destruction, such as military blockades or occupations. Future work should include integrating climate change models, crops yield models, and water resource allocation models to protect the key areas. Furthermore, interdisciplinary approaches are crucial for overcoming the limitations of VW research and these findings will provide valuable insights to enhance the optimal regulation of VWTNs.

Percolation of conditional dependency clusters based on edge-coupled interdependent networks

Considering the existence of multiple edge dependencies in realistic interdependent networks, we propose a model of edge-coupled interdependent networks with conditional dependency clusters (EINCDCs). In this model, the edges in network A depend on the edges in dependency clusters of size m in network B. If the failure rate of edges within the dependency clusters in network B exceeds the failure tolerance α, the corresponding edges in network A that depend on those clusters in network B will fail accordingly. By adopting the self-consistent probabilities approach, a theoretical analytical framework is established to quantitatively address this model. Specifically, we study the robustness of the system verified with numerical simulations in the effect of the cluster size and failure tolerance under random attacks on systems composed of two networks A and B constructed with Random Regular (RR), Erdös-Rényi (ER) and Scale Free (SF) models. Our results show that both networks A and B undergo a first-order or hybrid phase transition when the dependency cluster size does not exceed 2. However, when the cluster size of dependency clusters exceeds 2, a non-monotonic behavior is observed. In particular, when the failure tolerance is in the range from 0 to 0.5, the robustness of the system weakens with the growing in the number of dependency clusters of size 2. While, this tendency reverses when the failure tolerance is in the range from 0.5 to 1. Moreover, we observe that due to the asymmetric interdependency between the two networks, network B always undergoes first-order phase transition, whereas network A could exhibit different types of phase transitions, which depends on the size of dependency clusters. In addition, the failure tolerance may have opposite effects on the two networks with the growing of dependency cluster sizes. The conclusions of the study may provide useful implications and enrich the understanding in the robustness of edge-coupled interdependent networks.

A novel adaptive event‐triggered security consensus control mechanism for leader‐following multi‐agent systems under hybrid random cyber attacks

AbstractAiming at the security consensus control problem of leader‐following multi‐agent systems (MASs) under hybrid random cyber attack, this article proposes a novel sampled information related adaptive event‐triggered control mechanism (SIRAETCM). While ensuring the safety performance of the MASs, the mechanism adaptively and dynamically adjusts the trigger threshold of every agent to achieve discontinuous communication by using only the current and latest sampled signals. According to the MASs communication mode and Bernoulli attack model, a security consensus control protocol is constructed, and a bilateral sampled‐interval Lyapunov functional (BSILF) method is introduced to obtain more sampling interval information and establish sufficient conditions for the leader‐following state error system to stabilize asymptotically under hybrid random cyber attacks. Meanwhile, under a large sampling interval, the controller gain and adaptive event‐triggered parameters are designed and obtained. The simulation of the tunnel diode circuit system shows that the SIRAETCM can reduce the number of communications between agents to improve bandwidth utilization, and the adopted safety cooperative control protocol can improve the safety and effectiveness of the MASs.

Spatial Structure and Vulnerability of Container Shipping Networks: A Case Study in the Beibu Gulf Sea Area

Ports play an important role in maintaining the effectiveness of maritime logistics. When ports encounter congestion, strikes, or natural disasters, the maritime container transportation network might be significantly affected. The Beibu Gulf sea area is a key channel to supporting China’s participation in international economic cooperation in the western region. It is highly susceptible to the influence of the political and economic instability. This study introduces a dual-component framework to analyze the inherent structure and potential vulnerabilities of the container transportation network in the Beibu Gulf Sea areas. The findings show that the core layer of the network exhibited circular solidification characteristics. The entire network heavily relies on some core ports, such as Haiphong Port, Ho Chi Minh Port, and Qinzhou Port, and it highlights the potential increases in vulnerability. The finding shows that deliberate attacks have a greater impact than random attacks on the normal operations of maritime networks. If ports with high intermediary centrality are attacked, the connectivity and transportation efficiency of the Beibu Gulf maritime network will be significantly affected. However, under such circumstances, redistributing cargo transportation through route adjustments can deal with the transmission of cascading failures and maintain the network’s resilience. Based on the existing knowledge and the data collected in a case study, this research stands out as the first to provide a critical examination of the spatial structure and vulnerability of container shipping networks in the Beibu Gulf sea.

A Sampled-Data-Based Secure Control Approach for Networked Control Systems Under Random DoS Attacks.

This article aims to analyze H∞ stability of a class of networked control systems (NCSs) under random denial of service (DoS) attacks and design a sampled-data-based state feedback security controller to mitigate the influence of attacks. Different from the existing random attacks, the information about the maximum duration time of DoS attacks can be captured by introducing a predesigned logical processor. Then, based on the periodic sampling technique, the probability of attack occurrence and the resultant number of maximum allowable consecutive packet dropouts can be calculated, which is quite significant to investigating the security problem of NCSs. A DoS-dependent security controller which makes full use of the attack probability information and the number of attack-induced packet dropouts is designed. A novel networked sampled-data system model is first established that enables us to deal with the random DoS attacks phenomena and the time-varying delay induced by attacks under a uniform framework. By structuring a suitable Lyapunov-Krasovskii functional, the relationship between mean square asymptotic stability and attack characteristics is obtained. Finally, the reliability and applicability of the presented control strategy in eliminating the influence of DoS attacks are validated by two practical engineering applications.

Evaluation of ecological network resilience using OWA and attack scenario simulation in the Gansu section of the Yellow River Basin, NW China

Regional ecosystem quality has been rapidly declining as a result of rapid urbanization, which has also fragmented landscapes and reduced ecosystem connectedness. Strengthening ecological network resilience helps improve the ecological environment’s quality, protect biodiversity, and maximize ecological benefits. To quantitatively assess ecological network resilience based on 2020 data, we have selected the Gansu region of the Yellow River Basin as a case study in this study due to its significant ecological condition and sensitive vulnerability. Firstly, based on four ecosystem services (ESs)—water yield, carbon storage, soil conservation, and habitat quality—the ordered weighted averaging (OWA) method is introduced to determine ecological sources by calculating ordered weights under different risk coefficients to weigh multiple ESs. Two node attack simulations—random attack and deliberate attack—are used for quantitatively evaluating ecological network resilience, which can simulate the impact of external interference. The concept of a resilience threshold is introduced into the evaluation process of network resilience to improve the objectivity and accuracy of results. The findings indicate that (1) 156 ecological corridors spanning a total distance of 6,569.3 km and 73 ecological sources totalling 20,840 km2 were found in the study area. These findings generally demonstrate a concentrated and contiguous configuration in the southwestern region and a broken and scattered configuration in other regions. (2) With a mean degree of 4.27, a mean path length of 4.08, and a clustering coefficient of 0.47, an undirected and unweighted complex network with improved connectivity and no discernible clustering characteristics was established. (3) The ecological network is more resilient to perturbations from natural disasters when compared to assault simulations in two scenarios; the resilience threshold is 0.34. Considering the final results of node global features and resilience curves, ecological protection suggestions are proposed.

Resilience evaluation and enhancing for China’s electric vehicle supply chain in the presence of attacks: A complex network analysis approach

Over the past decade, the electric vehicle (EV) industry has experienced rapid growth, necessitating a deeper understanding of its supply chain network’s structure and resilience. This study presents an analysis into China’s EV supply chain network through a two-tier network, employing complex network approach to explore its topological characteristics and network’s resilience. The results reveal that the nodes within China’s EV network follow a power-law distribution, with shorter average path lengths compared to other automobile networks, indicating a concentration of connections upstream. The resilience assessment shows that while the network is resilient against random attacks, it is significantly vulnerable to targeted attacks. Importantly, the High-Betweenness Addition (HBA) strategy is identified as a better strategy for improving the network’s overall resilience. This strategy of reinforcing the centrality of core nodes within the supply chain is highlighted as a crucial approach for enhancing the network’s resilience. Our findings underscore the importance of developing strategic connections among high-betweenness nodes to distribute these enhancements evenly, aiming to add one or two edges for enhancing network resilience. This study offers a lucid roadmap for enhancing the resilience of China’s EV supply chain network in the face of emerging challenges.

Memory‐based event‐triggered control for networked control system under cyber‐attacks

AbstractThis article focuses on the problem of stability for a class of linear networked control systems (NCSs) subjected to network communication delays and random deception attacks. A new memory event‐triggered mechanism (METM) is proposed to reduce the unnecessary transmitted data through the communication channel and then enhance the network resources. In this context, a new memory stochastic state feedback controller is proposed to stabilize the closed‐loop networked control system. A new randomly occurring deception attacks model is employed to deal with the security problem of NCSs. Sufficient stability conditions are derived based on a suitable Lyapunov‐Krasovskii functional (LKF). The designed methodology is proposed in terms of linear matrix inequality to synthesize both event‐triggered parameters and controller gains, and to reduce the conservatism of the system some integral lemma are exploited to bind the time derivative of the LKF. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed method which provides a maximal upper bound value of the network‐induced delay and less transmitted packet regarding the maximal value delay obtained in other works, so less conservatism results are obtained, compared to previous ones in the literature.

Study on the Stability of Complex Networks in the Stock Markets of Key Industries in China.

Investigating the significant "roles" within financial complex networks and their stability is of great importance for preventing financial risks. On one hand, this paper initially constructs a complex network model of the stock market based on mutual information theory and threshold methods, combined with the closing price returns of stocks. It then analyzes the basic topological characteristics of this network and examines its stability under random and targeted attacks by varying the threshold values. On the other hand, using systemic risk entropy as a metric to quantify the stability of the stock market, this paper validates the impact of the COVID-19 pandemic as a widespread, unexpected event on network stability. The research results indicate that this complex network exhibits small-world characteristics but cannot be strictly classified as a scale-free network. In this network, key roles are played by the industrial sector, media and information services, pharmaceuticals and healthcare, transportation, and utilities. Upon reducing the threshold, the network's resilience to random attacks is correspondingly strengthened. Dynamically, from 2000 to 2022, systemic risk in significant industrial share markets significantly increased. From a static perspective, the period around 2019, affected by the COVID-19 pandemic, experienced the most drastic fluctuations. Compared to the year 2000, systemic risk entropy in 2022 increased nearly sixtyfold, further indicating an increasing instability within this complex network.

An adaptive trimming approach to Bayesian additive regression trees

A machine learning technique merging Bayesian method called Bayesian Additive Regression Trees (BART) provides a nonparametric Bayesian approach that further needs improved forecasting accuracy in the presence of outliers, especially when dealing with potential nonlinear relationships and complex interactions among the response and explanatory variables, which poses a major challenge in forecasting. This study proposes an adaptive trimmed regression method using BART, dubbed BART(Atr) to improve forecasting accuracy by identifying suspected outliers effectively and removing these outliers in the analysis. Through extensive simulations across various scenarios, the effectiveness of BART(Atr) is evaluated against three alternative methods: default BART, robust linear modeling with Huber’s loss function, and data-driven robust regression with Huber’s loss function. The simulation results consistently show BART(Atr) outperforming the other three methods. To demonstrate its practical application, BART(Atr) is applied to the well-known Boston Housing Price dataset, a standard regression analysis example. Furthermore, random attack templates are introduced on the dataset to assess BART(Atr)’s performance under such conditions.