Network Vulnerability Analysis Research Articles

Travel Characteristics and Vulnerability Analysis of Road Resource Utilization Based on Taxi GPS Data

Residents’ travel and logistics are greatly affected by urban transportation networks, which are one of the most important supports for urban socio-economic activities. Urban transportation systems tend to cripple when faced with challenges such as natural hazards and social unrest. This paper proposes a framework for a vulnerability analysis of urban road networks (URNs) based on real traffic flows with GPS data. An improved K-shell critical node identification method is proposed based on structural and traffic characteristics. Then, a cascade failure model is proposed to analyze the structural and functional vulnerability of the URN by combining the load capacity model and the vulnerability model. This paper takes the Harbin main city URN as an example and first analyzes the passenger travel distribution and the relationship between travel orders, population and POI. Four deliberate attack methods are proposed to analyze the vulnerability of the URN under deliberate attack on commute days and rest days. The experimental results show that URNs exhibit intense vulnerability, with the fastest cascading failure occurring based on improved K-shell node failure. Furthermore, URNs are more vulnerable on rest days compared to commuter days. These findings could be used to inform a vulnerability-based spatiotemporal design of UBNs and provide theoretical support for managing traffic congestion on different days.

Application of Artificial Intelligence Technology in Vulnerability Analysis of Intelligent Ship Network

The improvement in transportation efficiency, security, safety, and environmental effects may be possible due to the impending advent of autonomous ships. Automatic situational awareness, risk detection, and intelligent decision-making are the key features of the intelligent ship network, differentiating it from conventional ships. There is an immediate need to implement a system for marine information management and network security due to the growing importance of this field, which poses a risk to national and societal stability due to factors, such as the diversity and complexity of marine information types, the challenges associated with data collection, and other similar factors. By recognizing different vulnerabilities and through research cases of the ship systems and Artificial Intelligence (AI) technologies, this paper presents Adaptive Fuzzy Logic-assisted Vulnerability Analysis of Intelligent Ship Networks (AFL-VA-ISN) in various cyberattack scenarios for autonomous ship intrusion detection and information management. Fuzzy logic has been combined with AI, providing a framework for handling uncertainty and imprecision in intelligent ship networks and effective decision-making. This work presents a method for detecting anomalies in risk data based on the collaborative control structure of the Ship Information System. Maintaining the network security of intelligent ships is the primary focus of this research, which mainly employed multi-sensor nodes to evaluate data containing information about malicious attacks and placed self-execution protection organize generating nodes into place to intercept and protect against attacks. The experimental outcomes demonstrate that the suggested AFL-VA-ISN model increases the data transmission rate by 99.2%, attack detection rate by 98.5%, risk assessment rate by 97.5%, and access control rate of 96.3%, and reduces the network latency rate of 11.4% compared to other existing models.

Vulnerability Analysis of UAV Swarm Network with Emergency Tasks

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, UAV swarms are used for emergency tasks in various scenarios such as area detection, fire rescue, logistics, and transportation. However, for complex scenarios, UAV swarms are prone to environmental interference that damages their equipment or disrupts their communication links, affecting the normal execution of tasks. In this paper, an information–communication interdependent network model is designed for the vulnerability analysis of UAV swarm networks with emergency tasks. Firstly, from the perspective of network functions of a UAV swarm, we introduce the theory of interdependent networks to abstract the relationship between the UAV swarm’s communication network and its information network, where the communication network represents its communication topology and the information network is related to the function of each UAV individual in the UAV swarm. Then, the vulnerability of the UAV swarm is analyzed according to the relationship between network construction costs and network connectivity under environmental interference. Finally, the effectiveness of the vulnerability analysis method is verified through simulation.

NetVA: an R package for network vulnerability and influence analysis

In biological network analysis, identifying key molecules plays a decisive role in the development of potential diagnostic and therapeutic candidates. Among various approaches of network analysis, network vulnerability analysis is quite important, as it assesses significant associations between topological properties and the functional essentiality of a network. Similarly, some node centralities are also used to screen out key molecules. Among these node centralities, escape velocity centrality (EVC), and its extended version (EVC+) outperform others, viz., Degree, Betweenness, and Clustering coefficient. Keeping this in mind, we aimed to develop a first-of-its-kind R package named NetVA, which analyzes networks to identify key molecular players (individual proteins and protein pairs/triplets) through network vulnerability and EVC+-based approaches. To demonstrate the application and relevance of our package in network analysis, previously published and publicly available protein–protein interactions (PPIs) data of human breast cancer were analyzed. This resulted in identifying some most important proteins. These included essential proteins, non-essential proteins, hubs, and bottlenecks, which play vital roles in breast cancer development. Thus, the NetVA package, available at https://github.com/kr-swapnil/NetVA with a detailed tutorial to download and use, assists in predicting potential candidates for therapeutic and diagnostic purposes by exploring various topological features of a disease-specific PPIs network. Communicated by Ramaswamy H. Sarma

Enhancing Industrial Control Network Security Through Vulnerability Detection and Attack Graph Analysis

Insufficient communication attack defense capabilities within industrial control networks is a serious problem that is addressed in this study. The author proposes a methodology that focuses on creating attack graphs to ease security and vulnerability studies in industrial control network systems in order to address this issue. The article provides thorough construction guidance and techniques for attack graphs, which are used for penetration testing and vulnerability analysis of networks for industrial control systems. On the created attack graph, experimental evaluations utilizing the ``earthquake net'' virus were carried out. The findings point to four main attack routes where the ``Zhenwang'' virus is most likely going to attack and cause the most damage. With a loss value of 12.2 and an attack success chance of 0.096, the first path involves cumulative attack stages. The second path consists of cumulative attack steps, with a loss value of 10.2 and an attack success probability of 0.072. The third path encompasses cumulative attack steps, with a loss value of 16.6 and an attack success probability of 0.063. The fourth path comprises cumulative attack steps, with a loss value of 18.6 and an attack success probability of 0.084.

Evaluating the Vulnerability of the Sydney Train Network by Comparing Access-based and Network Centrality Metrics

Operational incidents are a significant cause of unreliability on rail transit networks. These incidents cause major delays in services, impact passenger travel time, and have knock-on effects that interrupt other public transport services. Consequently, the vulnerability of the rail transit network is a crucial concern for managers and operators. This paper employs network vulnerability analysis to characterize individual critical stations in a railway network. The concepts of graph theory and person-weighted access are implemented to identify the critical nodes in the Sydney train and metro network, and the results are compared. In the first method, weighted and unweighted centrality measures are computed to find the most critical station. In particular, eigenvector centrality is used to identify the critical nodes by scoring all nodes in the network using the first eigenvector of the graph adjacency matrix. In the second approach, stations are ranked by the reduction of access before and after an incident. Finding of this study may have implications not only for the train operators and managers but also for the transit network planners to enhance the resilience of the public transport network.

Morphometric Analysis of Trail Network and Tourist Vulnerability in a Highly Frequented Protected Area

Increasing interest in the natural environment and greater hiking activity have resulted in higher anthropogenic pressure in areas characterized by a geographic/physical setting that could present hazardous conditions. The development of these activities is influenced by the peculiar geomorphological and climatic conditions of the area. Visitors and hikers do not always have adequate cultural background and full awareness of natural dynamics, including the possibility of incurring hazardous conditions. For its cultural and landscape value and extraordinary trail network, the Portofino Promontory is frequented by more than a hundred thousand of hikers a year. However, due to the geomorphological characteristics of the area, the morphological features of the trail network (i.e., exposed paths, steep ups and downs, rocky sections with cables, etc.) and the peculiar meteo-climatic conditions, the number of accidents involving hikers has increased in the most recent years. This research uses a detailed LiDAR survey, a morphometric analysis, and a significant dataset of information on the frequentation of the hiking trail network and on the number of rescue operations carried out by the National Mountain Rescue and Speleological Service (CNSAS). These data have been related to the physical-geographical characteristics of the area. The results can be a useful tool for land management by the Park Authority.

Isolation vulnerability analysis in road network: Edge connectivity and critical link sets

This article focuses on vulnerability in transportation systems because of large-scale disasters. We proposes an index of isolation vulnerability as a new metric for network vulnerability analysis. Isolation vulnerability covers the situation in which supporting bases and receiving bases in a network cannot connect when some links are degraded due to external forces imposed on the network. Isolation vulnerability is derived as a solution of the maximum flow problem for a one-to-one network extended for supporting bases and receiving bases. A single set of link combinations does not necessarily exist that defines an isolation vulnerability, i.e., a set that is isolation-critical. A method of enumerating all of these critical link sets is provided and applied to the road network in Sapporo city, Japan, to identify highly vulnerable public shelters and analyze their characteristics.

Identification of key molecular players and associated pathways in cervical squamous cell carcinoma progression through network analysis.

Cervical cancer is the primary cause of mortality among women in developing countries. Preventing cervical cancer is partially possible through early vaccination against the human papillomavirus, the most common cause of the disease. Nevertheless, it is imperative to understand the genetics of the disease progression to develop new therapeutic strategies. The present study aims to identify potential genes and associated pathways associated with cervical squamous cell carcinoma progression. We used an integrative approach by combining differential expression analysis, network biology, and functional enrichment analysis with survival analysis. In the present study, differential expression analysis of the microarray-based gene expression profiles of cervical cancer resulted in identifying a total of 544 significantly differentially expressed genes (DEGs). Further, centrality and network vulnerability analysis of the protein-protein interaction network (PPIN) and not well documented in cervical cancer, resulted in seven proteins (FN1, MCM5, TRIP13, KIF11, TTK, CDC45, and BUB1B), in which four proteins were vulnerable. These genes are mostly enriched in biological processes of cell division, mitotic nuclear division, cell cycle checkpoint, and cell proliferation in gene ontology analysis. The KEGG pathway enrichment analysis of the proteins lists them as mainly associated with the cell cycle. In the survival analysis, it was found that the genes MCM5, FN1, KIF11, and CDC45 were statistically significant prognostic factors for cervical cancer. The outcome of the current study identifies and explores the key role of the candidate genes involved in the progression of cervical cancer.

Node and Link Vulnerability in Complete Multipartite Networks

Networks are known to be prone to node or link failures. A central issue in the analysis of networks is the assessment of their stability and reliability. A central concept that is used to assess stability and robustness of the performance of a network under failures is that of vulnerability. Node and link residual closeness are novel sensitive graph based characteristics for network vulnerability analysis. Node and link residual closeness measure the vulnerability even when the removal of nodes or links does not disconnect the network. Node and link residual closeness are of great theoretical and practical significance to network design and optimization. In this paper, vulnerabilities of multipartite network type topologies to the failure of individual nodes and links are computed via node and link residual closeness which provides a much fuller characterization of the network. Then, how multipartite network type topologies perform when they suffer a node or a link failure is analyzed.

Power-grid vulnerability and its relation with network structure.

Interconnected systems with critical infrastructures can be affected by small failures that may trigger a large-scale cascade of failures, such as blackouts in power grids. Vulnerability indices provide quantitative measures of a network resilience to component failures, assessing the break of information or energy flow in a system. Here, we focus on a network vulnerability analysis, that is, indices based solely on the network structure and its static characteristics, which are reliably available for most complex networks. This work studies the structural connectivity of power grids, assessing the main centrality measures in network science to identify vulnerable components (transmission lines or edges) to attacks and failures. Specifically, we consider centrality measures that implicitly model the power flow distribution in power systems. This framework allow us to show that the efficiency of the power flow in a grid can be highly sensitive to attacks on specific (central) edges. Numerical results are presented for randomly generated power-grid models and established power-grid benchmarks, where we demonstrate that the system's energy efficiency is more vulnerable to attacks on edges that are central to the power flow distribution. We expect that the vulnerability indices investigated in our work can be used to guide the design of structurally resilient power grids.

Vulnerability analysis of urban road networks based on traffic situation

Traffic congestion is a global issue, which occurs during rush hour but also in situations of emergency causing massive congestion. This paper proposes a method for building a weighted road network by using the real-time traffic situation and the inherent characteristics of Urban Road Networks (URNs). To research the cascading failure vulnerability of URNs three kinds of node importance indexes are constructed from the structure, function, and traffic flow characteristics. Then the feasibility and validity of the proposed model are verified by taking Shanghai road networks (SRNs) as an example. The results indicate that the highest betweenness node-based attack causes the most damage to the SRNS of different types of attacks, and the SRNS cascade fails with the greatest speed and scale. Furthermore, we explore that the correlations between network vulnerability indicators, and suggest significant differences at different times during cascading failures of the weighted road network.

A Novel Approach for Network Vulnerability Analysis in IIoT

Industrial Internet of Things (IIoT) offers efficient communication among business partners and customers. With an enlargement of IoT tools connected through the internet, the ability of web traffic gets increased. Due to the raise in the size of network traffic, discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues. A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification (MPDQDJREBC) is introduced for accurate attack detection with minimum time consumption in IIoT. The proposed MPDQDJREBC technique includes feature selection and categorization. First, the network traffic features are collected from the dataset. Then applying the Maximum Posterior Dichotomous Quadratic Discriminant analysis to find the significant features for accurate classification and minimize the time consumption. After the significant features selection, classification is performed using the Jaccardized Rocchio Emphasis Boost technique. Jaccardized Rocchio Emphasis Boost Classification technique combines the weak learner result into strong output. Jaccardized Rocchio classification technique is considered as the weak learners to identify the normal and attack. Thus, proposed MPDQDJREBC technique gives strong classification results through lessening the quadratic error. This assists for proposed MPDQDJREBC technique to get better the accuracy for attack detection with reduced time usage. Experimental assessment is carried out with UNSW_NB15 Dataset using different factors such as accuracy, precision, recall, F-measure and attack detection time. The observed results exhibit the MPDQDJREBC technique provides higher accuracy and lesser time consumption than the conventional techniques.

Computational complexity of network vulnerability analysis

Abstract Residual closeness is recently proposed as a vulnerability measure to characterize the stability of complex networks. Residual closeness is essential in the analysis of complex networks, but costly to compute. Currently, the fastest known algorithms run in polynomial time. Motivated by the fast-growing need to compute vulnerability measures on complex networks, new algorithms for computing node and edge residual closeness are introduced in this paper. Those proposed algorithms reduce the running times to Θ(n3) and Θ (n4) on unweighted networks, respectively, where n is the number of nodes.

Flood Hazard Mapping and Road Network Vulnerability Analysis in Kamrup and Kamrup Metropolitan Areas: A Case Study of Assam Floods in 2019

Flood Hazard Mapping and Road Network Vulnerability Analysis in Kamrup and Kamrup Metropolitan Areas: A Case Study of Assam Floods in 2019

Accessibility-based vulnerability analysis of multi-modal transportation networks with weibit choice models

This study proposes utility-based accessibility measures and a weibit-based analysis method for analyzing multi-modal transportation network vulnerability. To account for the multi-dimensional travel choice behavior in multi-modal transportation networks, an advanced weibit-based combined modal split and traffic assignment model is adopted to derive the accessibility measures based on the weibit-expected travel disutility from both route choice and mode choice dimensions. The accessibility at the modal split level is derived from the nested weibit model, while that at the route choice level is obtained based on the path-size weibit model. The proposed weibit-based vulnerability measures are inherently suitable for assessing the relative degradation in network performance and can measure the vulnerability of networks with heterogeneous scales without overestimating the importance of modes or routes sharing similar features. Using a simplified multi-modal transportation network of Hong Kong, numerical experiments are conducted to demonstrate the properties of the proposed multi-modal vulnerability analysis. The results indicate that the proposed analysis can effectively address similarity and heterogeneity issues in the analysis of choice behavior and network vulnerability, which are often ignored by the traditional logit-based measures.

Who is the Weakest Link? A Network Vulnerability Analysis Using a Congested Transport Assignment

We propose a user-equilibrium congested transit assignment model for a full-scan network vulnerability analysis by relying on the computations of network science indicators for infrastructure and service graphs. Model output include line-specific passenger loads. Furthermore, we propose a link criticality indicator for measuring the spatial extent of spill-over effects of link closures. In the case of the Amsterdam a disruption on the most critical link in the peak period induces 1,000 passenger-hours loss and a disruption spill overs on average to more than seven other links. Betweenness centrality and passenger loads do not offer good proxies for assessing link criticality.

An attack graph-based approach to network vulnerability analysis

As the global threat situation intensifies, it is difficult for traditional security strategies to effectively respond to complex attack methods. In this paper, we propose a value-aware and memorized attack graph generation method which successfully compresses the strategy space and effectively reduces the algorithm complexity. Through a virtual network experiment, we validate the feasibility of the proposed methods above.

Distributionally risk‐receptive and risk‐averse network interdiction problems with general ambiguity set

AbstractWe introduce generalizations of stochastic network interdiction problem with distributional ambiguity. Specifically, we consider a distributionally risk‐averse (or robust) network interdiction problem (DRA‐NIP) and a distributionally risk‐receptive network interdiction problem (DRR‐NIP) where a leader maximizes a follower's minimal expected objective value for either the worst‐case or the best‐case, respectively, probability distribution belonging to ambiguity set (a set of distributions). The DRA‐NIP arises in applications where a risk‐averse leader interdicts a follower to cause delays in their supply convoy. In contrast, the DRR‐NIP provides network vulnerability analysis where a network‐user seeks to identify vulnerabilities in the network against potential disruptions by an adversary (or leader) who is receptive to risk for improving the expected objective values. We present finitely convergent algorithms for solving DRA‐NIP and DRR‐NIP with a general ambiguity set. To evaluate their performance, we provide results of our extensive computational experiments performed on instances known for (risk‐neutral) stochastic NIP.

Industrial water network vulnerability analysis using dynamic inoperability input-output model

Industrial parks provide opportunities for Process Integration among different enterprises. Inter-Plant Water Network Integration is an effective strategy for water conservation. However, increased interplant linkages can make the entire system vulnerable to cascading failures in case of loss of water flow in some plants. The potential indirect impact of water shortages on such integrated systems may not be evident without the use of appropriate models. This work defines inoperability as the fractional loss of water flow relative to normal operations. A comparison between the applicability of demand-driven versus supply-driven Inoperability Input-output Model (IIM) is conducted. Then, a Vulnerability Assessment Framework which integrates vulnerability indicators into the Dynamic Input-Output Model (DIIM) is developed to analyse failure propagation in water networks in an industrial park. The DIIM is then applied to simulate the cascading effects of disturbances. From a time perspective, the vulnerabilities of the industrial parks With Integrated Optimal Water Network (WWN) and Without Integrated Optimal Water Network (WOWN) are assessed considering robustness, adaptability, and recoverability as the indicators. The results indicate that supply-driven IIM is more suitable for cascading failure analysis of water networks. The average inoperability at 16% from supply-driven IIM is higher than that from demand-driven IIM. In the freshwater disturbance scenario, the dependence of the plant on freshwater is proportional to the rate of inoperability change, the time to reach a new equilibrium. In this study, the robustness of WWN is about fivefold that of WOWN, but the recovery rate is only one-sixth of the latter. This work can help identify system vulnerabilities and provide a scientific insight for the development of park-wide water management strategies.