Intentional Attacks Research Articles

Environmental consequences of Russian aggression invasion of Ukraine (2022)

Introduction. Environmental protection, rational use of natural resources and environmental security are among the guidelines for sustainable development of any state. However, it is important to emphasize that effective realization of such activities is possible only in peaceful time. At present, the full-scale Russian invasion of Ukraine puts under the direct threat effective ensuring of environmental security. The purpose of the paper is to analyze the environmental impact of the 2022 Russian invasion of Ukraine, highlight its consequences and predict the future environmental situation. Results. Under international law, an environmental crime is interpreted as an intentional attack when it is known that it will cause significant, long-term and serious damage to the environment, which is clearly disproportionate to the specific and directly expected overall military superiority. The military action of the Russian Federation in Ukraine in 2022 is undoubtedly an unjustified and unprovoked act of aggression against independent and sovereign Ukraine, as well as a violation of all existing norms of international law on environmental protection, sustainable development, humanitarian law, basic morals and principles of human coexistence. By May 2022 27 254 cases of environmental war crimes with regards to Russia's war against Ukraine had been registered. These include shelling and bombing of industrial and energy facilities, forest fires, sabotage of oil depots, pollution of the seas, destruction of reserves, as well as other horrific, inhumane actions of the Russian army. Conclusion. Russia's aggression against Ukraine has caused significant damage to the environment, like deterioration of soil, radiation pollution, biodiversity losses, forest fires, ecosystems’ destruction, etc. In order to remedy the current dire environmental situation, the development and implementation of urgent legal and policy measures is required in order to minimize the harmful environmental effects. In particular, it is necessary to constantly keep records of all crimes against the environment committed on the territory of Ukraine, and to record the damage caused. In addition, a special attention should be paid to the development of a plan for the reconstruction of Ukraine, which would include measures to restore and preserve the environment.

Identification and stability analysis of critical ecological land: Case study of a hilly county in southern China

Critical ecological land is indispensable for ecosystem stability, ecosystem service provision, and ecosystem function. Assessing and monitoring the stability of critical ecological land is an effective strategy of ensuring regional ecological security. The present study focused on Xinfeng County, China, a southern hilly region, as the study area, and evaluated water conservation, soil and water conservation, biodiversity conservation, leisure and sightseeing functions, in addition to land use status, in 2000, 2010, and 2020. Afterward, ecological ecosystem service function was evaluated to identify critical ecological land. A complex network method was used to analyze ecological network stability to more comprehensively analyze characteristics of critical ecological land in time and space. According to the results, the landscape types of critical ecological land in 2000, 2010, and 2020 were primarily forest land and grassland, with the majority being forest land with high vegetation density and cover, validating the accuracy and feasibility of our method. Under the “random attack” scenario, the robustness of network connectivity in 2000, 2010, and 2020 had a consistent decreasing trend. However, under the “intentional attack” scenario, connectivity robustness changes more intense in 2010 and 2020, and the ecological network in 2020 shows instability and fragility. Additionally, according to the spatial distribution characteristics of critical ecological land, a network protection strategy is put forward to guide and support subsequent ecological protection work.

Preferential attachment, R&D expenditure and the evolution of international trade networks from the perspective of complex networks

From the perspective of complex networks, whether international trade networks (ITNs) can remain robust and connected in the face of random emergencies or intentional attacks is an important indicator of the security of international trade. Thus, we introduced the scale-free property to describe it. Using trade data from 1996–2018 for 126 economies, we constructed ITNs and investigated their scale-free property. Our results reveal that ITNs are not scale free in most years and that they become decentralized. In addition, our study explored the evolution of ITNs using the temporal exponential random graph model (TERGM) and verified the nonexistence of the preferential attachment mechanism that drives the formation of scale-free networks. Furthermore, we emphasized the impact of different R&D expenditure levels on the formation of trade relationships. Economies with medium (low) levels of R&D expenditures are less connected to those with the same level, while economies with high levels of R&D expenditures prefer to establish trade relationships with those with the same level. This finding indicates that the establishment of trade relationships could occur more often between economies with a high level of R&D expenditures and economies with a medium (low) level of R&D expenditures or among economies with a high level of R&D expenditures. Therefore, economies with a high level of R&D expenditures are more active and attractive when selecting trade partners. Overall, we extend the exploration in the field of ITN research and provide an innovative analysis framework to study real networks.

Topology and Robustness of Weighted Air Transport Networks in Multi-Airport Region

Topological analyses of multi-airport regional air transport networks are the basis for the sustainable development of multi-airport systems. In this study, we modeled the Yangtze River Delta (YRD) region’s airport infrastructure as a network and presented a weighted approach by which to analyze the network structure and robustness from the perspective of complex network theory. The analysis of the Yangtze River Delta Airport Network (YRDAN) indicates that it is a small-world network, and its cumulative degree has a power-law distribution, suggesting that it has scale-free properties. As its weighted clustering coefficient was found to be much smaller than the non-weighted counterpart, this demonstrates that most of the network traffic is focused on a hub-and-spoke pattern. Furthermore, the over-centrality of the YRDAN suggests weak accessibility of small cities and high dependence of air transport on the hub-and-spoke pattern. The assessment of the robustness of the YRDAN in the face of intentional attacks found that domestic networks are more robust than foreign aviation networks. However, the isolation of a small fraction of selected nodes can cause serious problems in the functioning of the YRDAN.

Percolation behaviors of a network of networks under intentional attack with limited information

As an important class of real coupled networks, a network of networks portrays the interdependence and coexistence between complex systems. Especially for large networks, one may not know the information of all nodes in the network, only limited information can be known. Motivated by this, we here propose a new attack strategy, the intentional attack with limited information, where the limited information represents that the information of only n nodes is known. Further, we investigate the percolation behavior of large-scale network systems, a network of networks with different dependency patterns, under intentional attack with limited information. We show analytically and numerically how the coupling strength q and n affect the percolation, critical threshold, and the critical coupling strength. Furthermore, as n increases, the results suggest that the system becomes more vulnerable and different to protect. But when n reaches a critical value, the critical threshold pc tends to a steady-state and doesn't change appreciably with n. In particular, we find a new general scaling relationship between 1/pc and 1/n for different network configuration. Our model sheds light on the resilience of large-scale interdependent networks under limited information attacks, and provides helpful insights into designing robust real-world systems.

A ‘Divide and Embed’ approach in a robust crypto-watermarking technique for enhancing the embedding capacity

Medical Image watermarking techniques have a major concern of addressing the issue of embedding capacity as the electronic health record (EHR) of a patient is large in size. Embedding large size watermark into medical images degrades the perceptual quality of the image which may lead to changes in morphological features of the tissues or organs. The main aim of the proposed work is to address the challenge of the trade-off between embedding capacity and image quality along with maintaining robustness. A ‘Divide and Embed’ approach is proposed to enhance the embedding capacity without affecting the image perceptibility and robustness. This approach is based on the availability of multiple medical images with a single patient’s record. The patient information used as a watermark is divided into several parts equal to the number of medical images available with a patient record and embedded separately using a region-based crypto-watermarking technique with a wavelet-based hybrid embedding process. The projected technique is tested by varying embedding capacity to check for both imperceptibility and robustness against various unintentional and intentional attacks for both CT scan and MR Images. The PSNR achieved is 77.9 dB for the higher embedding capacity of 56,548 pixels by maintaining the robustness with average BER to 0.0609 and average NC to 0.9406 after applying various attacks. Therefore, the proposed approach has enhanced the embedding capacity by maintaining the perceptual quality of the medical image as well as robustness is also consistent as compared to the other researcher’s work.

Percolation of interdependent networks with limited knowledge.

Real-world networks are often not isolated and the interdependence between different networks in a complex system is as important as the topological connectivity within individual networks. We develop a theoretical framework to study the robustness of interdependent networks under attacks with limited knowledge. A node may be attacked if it is the most connected node among a given number of potential victims. This number is referred to as the attacker's knowledge level, which joins the two ends, namely, the random failure with zero knowledge and the intentional attack with full knowledge of the network. We introduce percolation models with attacks over one layer and two layers as well as mixed site-bond percolation. Along with the discontinuous phase transition, we show the existence of a critical knowledge level, which indicates a transition of network robustness under the competition between connectivity and interdependence. It is unraveled that interdependent networks can be extremely fragile to the extent that a random failure on two layers would be more deleterious than a targeted attack with full knowledge over one layer. Moreover, we find that a balanced distribution of attack knowledge on both layers tends to be most destructive if the total knowledge is a conserved quantity.

Percolation behaviors of finite components on complex networks

Percolation behavior is of wide applicability and provides insight into functional structure of complex networks. Different from the percolation behaviors of giant component (GC), the finite components make one more clearly explore network percolation behaviors and critical phenomena from a microscopic perspective, especially for large-scale network systems. Here we focus on the percolation behaviors of small component π s with the size s = 1, 2, 3, … under different failure scenarios such as random attack, localized attack, target attack and intentional attack with limited knowledge. We find theoretically and via simulation that finite components all show the peak shape which is different from GC for random networks including random regular network, Erdős–Rnyi networks and scale-free networks. In particular, we find a new general scaling relationship between and , p max represents the value of p (non-failure fraction of initial nodes) corresponding to the peak point of π s in the network. This finding also provides a potential approach for determining the critical threshold and fill the gap between finite components and GC on the percolation process.

Projectile perforation models for the vulnerability assessment of atmospheric storage tanks

Chemical and process plants storing large quantities of hazardous materials (flammable and/or toxic) may be attractive targets of intentional malicious attacks threatening the life and health of workers and population, as well as the environment and the availability of critical infrastructures. Quantification of the vulnerability of industrial facilities against shooting attacks received scarce attention in the context of Security Vulnerability Assessment (SVA) or Security Risk Assessment (SRA) methodologies. Specific damage models for projectile impact on equipment items storing relevant quantities of hazardous materials are not available in the literature. The current study aims at filling this gap by reviewing a wide set of empirical and analytical perforation models and validating them against experimental data from ballistic experimental tests. According to specific statistical parameters, the most suitable perforation models were selected in order to be used in the definition of quantitative methods for the analysis of the shooting attacks to atmospheric storage equipment. The application of the selected models to the assessment of the vulnerability to perforation of steel atmospheric storage tanks showed important differences between handguns and rifles as regards the thickness that can be successfully perforated. The results allowed for the definition of inherent safety thresholds for the perforation thickness that can be used in the context of vulnerability assessment of critical assets within SVA/SRA.

Robustness analysis of cyber-coupled power systems with considerations of interdependence of structures, operations and dynamic behaviors

The failure propagation of a networked system is profoundly affected by the operation and dynamic behavior of its constituent systems. In this paper, we propose a novel network-based model to investigate the cascading failure of cyber-coupled power systems. Unlike existing models that assume oversimplified network functions, operational characteristics and failure evolution, our proposed model considers the practical differences between a communication network and a power network in terms of network structure, physical operation and dynamic behavior. A practical trip mechanism is derived according to the instantaneous data transmission overload and the stochastic power flow overload. The effects of communication network connection strength and coupling patterns on the robustness of interdependent systems are also studied. Our study shows that the thermal memory effect of power lines aggravates the failure propagation, and that the robustness of the interdependent system is strongly related to the coupled communication network’s tolerance. Furthermore, coupling patterns based on network similarity have better inhibition effects on cascading failure under random attacks, and node destructiveness is a suitable coupling similarity index that can be used to improve the robustness of the system. However, the strategy is inapplicable when the system is under intentional attacks.

A demand-based framework for resilience assessment of multistate networks under disruptions

Multistate networks composed of many independent components are susceptible to natural disasters and intentional attacks. Resilience is described as the capability of keeping and enhancing the required functionality under disruptions, which is of great importance to design and analyze multistate networks. While the resilience engineering community makes great contributions to resilience assessment, approaches for measuring network performance are limited and the practical demand of users is neglected in multistate networks. This paper proposes a demand-based framework for resilience assessment under disruptions in order to address these issues. After specifying network structure with the help of reliability block diagram, component behavior is modeled as a Markov reward process. Both the probability of staying in a particular state and the cost of recovery actions can be calculated. Furthermore, network performance with multiple levels is quantified using universal generating function. Finally, network resilience is comprehensively evaluated from the perspectives of absorption, adaptation and recovery. Besides assessing overall resilience with the consideration of network demand, the proposed framework conducts quantitative analysis of multistate networks. A case study of the potable water network is used to demonstrate the applicability of the proposed framework.

Smart management system in electric market considering customer demand response

The case of a disruptive agent that tries to maximize damage to the system (determined by total network operating costs), while the system operator is taking the appropriate steps to minimize the consequences of the attack, is considered in the present study. A bi-level optimization problem is used to model the interaction between the two agents. One aspect of this is that the destructive factor is involved in the top-level optimization problem and should choose that lines and generators to disable (with a restricted destructive budget). Therefore, a new scheme is proposed for maximizing the system's resilience under intentional attacks, by implementing distributed energy resources (DERs) such as distributed generation, and demand response. Also, the grey wolf optimization algorithm is used to solve the optimization problem in the paper. By allocating DERs in islands produced by destroyed lines and generators, three metrics have been suggested in order to evaluate resilience.

Resiliency enhancement of power system against intentional attacks

Natural disasters, man-made attacks, and cyber-attacks are some of the main hazards of power system which can interrupt the process of continuous power delivery to the end users. In this way, man-made attacks characteristics’ are fundamentally different from other hazards due to its adaptive strategy that allows attackers to target the most vulnerable parts of the power system. By choosing appropriate time and place; attackers always have advantages over the defender to penetrate the system and launch malicious actions. Therefore, it is quite necessary to take into account the optimal policy for allocating resources in the defender's strategy. This research proposes a four-level Defence–Defence–Attack–Defence (DDAD) approach to improve the power system resilience against the intentional human attacks. A new defence level, a planning level, is added to the common three-level Defence–Attack–Defence (DAD) model in power system planning state in order to prevent cut off loads and voltage drop beyond standard with lower cost. In the proposed method, load priority is used to classify the vulnerable sections. The proposed model is performed on IEEE-30 bus test system and the results indicate the feasibility of the proposed method. As the results indicate, in proposed method less than 10% budget is needed compared with the previous method while all critical loads will be remained. Furthermore, the new method needs no security guard to protect transmission lines for 30 years.

Vulnerability analysis of the Chinese coupled aviation and high-speed railway network

The integrated aviation and High-Speed Railway (HSR) transportation system plays a vital role for today’s inter-city transportation services. However, an increasing number of unexpected disruptions (such as operation failures, natural disasters, or intentional attacks) pose a considerable threat to the normal operation of the system, especially on ground transfer, leading to the extensive research on its vulnerability. Previous approaches mainly focus on interruptions within a single transportation mode, neglecting the role of ground transfer which serves as a coupled connection between aviation and High-Speed Railway. This paper proposes a network-based framework for evaluating the vulnerability of the Chinese Coupled Aviation and High-Speed Railway (CAHSR) network from the viewpoint of ground transfer interruption. Taking the end-to-end travel time and passenger flow information into consideration as an evaluation measure and analyzing from the perspective of urban agglomerations, an adaptive method is developed to identify the critical cities and further investigate their failure impacts on the geographic distribution of vulnerability. In addition, the proposed model explores variations of vulnerability under different failure time intervals. Based on the empirical study, some major conclusions are highlighted as follows: (A) Only a few cities show significant impacts on the network’s vulnerability when ground transfer interruptions occurred. (B) The distribution of vulnerability is not proportional to the distance between failure city and influenced city. (C) The vulnerability is more serious in the morning and evening when the ground transfer is disconnected. Our findings may provide new insights for maintenance and optimization of the CAHSR network and other real-world transportation networks.

Study on Power Grid Partition and Attack Strategies Based on Complex Networks

Based on the community discovery method in complex network theory, a power grid partition method considering generator nodes and network weightings is proposed. Firstly, the weighted network model of a power system is established, an improved Fast-Newman hierarchical algorithm and a weighted modular Q function index are introduced, and the partitioning algorithm process is practically improved combined with the characteristics of the actual power grid. Then, the partition results of several IEEE test systems with the improved algorithm and with the Fast-Newman algorithm are compared to demonstrate its effectiveness and correctness. Subsequently, on the basis of subnet partition, two kinds of network attack strategies are proposed. One is attacking the maximum degree node of each subnet, and the other is attacking the maximum betweenness node of each subnet. Meanwhile, considering the two traditional intentional attack strategies, that is, attacking the maximum degree nodes or attacking the maximum betweenness nodes of the whole network, the cascading fault survivability of different types of networks under four attack strategies is simulated and analyzed. It was found that the proposed two attack strategies based on subnet partition are better than the two traditional intentional attack strategies.

Urban Transportation Networks Resilience: Indicators, Disturbances, and Assessment Methods

Due to a surge in the frequency and intensity of disruptive events, such as natural disasters, the Covid-19 pandemic, and intentional attacks, the concept of resilience has attracted increased attention in recent years. Many scholars have focused on transportation network resilience because of its importance in society's well-being and recovery efforts after disturbances. Existing studies have suggested various definitions, indicators, and methods for assessing the resilience of transportation networks. This variation is due to differences in the nature, scale, and impact of disturbances. This systematic literature review presents resilience assessment methods for transportation networks, indicators, and disturbance categories. A new representation is suggested for the relationships between performance, time, and resilience, emphasizing other network characteristics and their association with resilience. Resilience indicators are categorized, and disturbance categories are analyzed. Approaches are grouped based on their methodologies and presenting their strengths and limitations. This paper directs future studies toward investigating emerging threats, including climate change, pandemics, cybersecurity, failure propagation, and the impacts of new technologies and paradigms on urban transportation resilience. Additionally, it highlights the benefits of identifying a reference metric. Finally, this paper promotes resilience-based thinking to address challenges facing cities worldwide as a cornerstone for creating lasting sustainable development.

A Novel Four-Level Approach for Improving Power System Resilience Against Intentional Attacks

This study proposes a new four-level model to enhance power system resilience against human attacks. The model considers human attacks, such as intelligent attackers and power system planners, as defenders. The attackers want to maximize the impacts of actions, while the defender tries to avoid imposed costs and damages due to budget limitations. To this end, a new four-level model defense-defense-attack-defense (DDAD) is proposed, in which a new defense layer is added to the common three-level model defense-attack-defense (DAD). Looking at the model more closely, new power plants and substations are added to the power system to improve its resilience, while the most important power plants and substations are determined. Subsequently, the conventional three-level model was applied. In this research, the defender and attacker have some strategies with their own costs for every power system component, such as power plants and substations, and their own interactions. The power system model includes the load, power plant and substation, and substation priority, which are defined as a combination of the value of the load and network topology. The proposed model was applied to the IEEE-30 bus test system, and the results indicated the effectiveness of the proposed method.

Artificial Intelligence Based Threat Detection in Industrial Internet of燭hings燛nvironment

Internet of Things (IoT) is one of the hottest research topics in recent years, thanks to its dynamic working mechanism that integrates physical and digital world into a single system. IoT technology, applied in industries, is termed as Industrial IoT (IIoT). IIoT has been found to be highly susceptible to attacks from adversaries, based on the difficulties observed in IIoT and its increased dependency upon internet and communication network. Intentional or accidental attacks on these approaches result in catastrophic effects like power outage, denial of vital health services, disruption to civil service, etc., Thus, there is a need exists to develop a vibrant and powerful for identification and mitigation of security vulnerabilities in IIoT. In this view, the current study develops an AI-based Threat Detection and Classification model for IIoT, abbreviated as AITDC-IIoT model. The presented AITDC-IIoT model initially pre-processes the input data to transform it into a compatible format. In addition, Whale Optimization Algorithm based Feature Selection (WOA-FS) is used to elect the subset of features. Moreover, Cockroach Swarm Optimization (CSO) is employed with Random Vector Functional Link network (RVFL) technique for threat classification. Finally, CSO algorithm is applied to appropriately adjust the parameters related to RVFL model. The performance of the proposed AITDC-IIoT model was validated under benchmark datasets. The experimental results established the supremacy of the proposed AITDC-IIoT model over recent approaches.

Vulnerability Evaluation and Improvement Method of Civil Aviation Navigation Network

Due to events such as natural disasters and navigation equipment failures, enormous calamity may be caused by the interruption of the navigation network which is a guarantee for the flight safety of civil aviation aircraft. The navigation network consists of the navigation stations as nodes and the routes between them as edges. Different nodes have different effects on the vulnerability of the network due to their different abilities to maintain the stability of the network topology and the normal function of the network. To quantify this difference and identify key nodes that have a greater impact on the vulnerability of the navigation network, an indicator to assess the importance of a navigation station is proposed which combines the structural importance reflected by node topology centrality and functional importance reflected by node weight. The structural importance of a node corresponds to its topology features including local dominance of the node and its global influence, and the important contribution to both adjacent and nonadjacent nodes from this node, while the functional importance is indicated by the flight flow serviced by the node during a fixed period of time. Vulnerability evaluation shows that the navigation network is more vulnerable when subject to the intentional attack of nodes with higher comprehensive node importance than an intentional attack of nodes with a larger value of indicators used in previous literature. Finally, the vulnerability of the navigation network is improved through changing the topology of the most critical node and balancing the node importance of the whole network.

Cascading Failure Analysis on Shanghai Metro Networks: An Improved Coupled Map Lattices Model Based on Graph Attention Networks.

Analysis of the robustness and vulnerability of metro networks has great implications for public transport planning and emergency management, particularly considering passengers’ dynamic behaviors. This paper presents an improved coupled map lattices (CMLs) model based on graph attention networks (GAT) to study the cascading failure process of metro networks. The proposed model is applied to the Shanghai metro network using the automated fare collection (AFC) data, and the passengers’ dynamic behaviors are simulated by GAT. The quantitative cascading failure analysis shows that Shanghai metro network is robust to random attacks, but fragile to intentional attacks. Moreover, there is an approximately normal distribution between instant cascading failure speed and time step and the perturbation in a station which leads to steady state is approximately a constant. The result shows that a station surrounded by other densely distributed stations can trigger cascading failure faster and the cascading failure triggered by low-level accidents will spread in a short time and disappear quickly. This study provides an effective reference for dynamic safety evaluation and emergency management in metro networks.