Network Vulnerability Analysis Research Articles

Vulnerability analysis of water distribution networks to accidental pipe burst

Even the best-maintained water distribution network (WDN) might suffer pipe bursts occasionally, and the utility company must reconstruct the damaged sections of the system. The affected area must be segregated by closing the corresponding isolation valves; as a result, the required amount of drinking water might not be available. This paper explores the behaviour and topology of segments, especially their criticality from the viewpoint of the whole system. A novel, objective, dimensionless, segment-based quantity is proposed to evaluate the vulnerability of both the segments and the whole WDN against a single, incidental pipe break, computed as the product of the probability of failure within the segment and the amount of unserved consumption. 27 comprehensive real-life WDNs have been examined by means of the new metric and with the help of complex network theory, exploiting the concept of the degree distribution and topology-based structural properties (e.g. network diameter, clustering coefficient). It was found that metrics based purely on topology suggest different network behaviour as vulnerability analysis, which also includes the hydraulics. The investigation of the global network vulnerabilities has revealed several critically exposed systems, and the local distributions unveiled new properties of WDNs in the case of a random pipe break.

Assessing potential likelihood and impacts of landslides on transportation network vulnerability

As one of the devastating natural disasters, landslide may induce significant losses of properties and lives area-wide, and generate dramatic damages to transportation network infrastructure. Accessing the impacts of landslide-induced disruptions to roadway infrastructure can be extremely difficult due to the complexity of involved impact factors and uncertainties of vulnerability related events. In this study, a data-driven approach is developed to assess landslide-induced transportation roadway network vulnerability and accessibility. The vulnerability analysis is conducted by integrating a series of static and dynamic factors to reflect the landslide likelihood and the consequences of network accessibility disruptions. The analytical hierarchy process (AHP) model was developed to assess and map the landslide likelihood. A generic vulnerability index (VI) was calculated for each roadway link in the network to identify critical links. Spatial distributions of landslide likelihood, consequences of network disruptions, and network vulnerability degrees were fused and analyzed. The roadway network on Oahu Island in Hawaii is utilized to demonstrate the effectiveness of the proposed approach with all the geo-coded information for its network vulnerability analysis induced by area-wide landslides. Specifically, the study area was classified into five categories of landslide likelihood: very high, high, moderate, low, and stable. About 34% of the study area was assigned as the high or very high categories. The results of network vulnerability analyses highlighted the importance of three highway segments tunnel through the Ko‘olau Range from leeward to windward, connecting Honolulu to the windward coast including the Pali highway segment, Likelike highway segment, and Interstate H-3 highway segment. The proposed network vulnerability analysis method provides a new perspective to examine the vulnerability and accessibility of the roadway network impacted by landslides.

Critical nodes identification for vulnerability analysis of power communication networks

As the support networks of the electric power grid, power communication networks (PCNs) become more complex and vulnerable due to the increasing scale of the electric power grid. Identifying and protecting the critical nodes in PCNs in advance is an effective way to reduce network vulnerability. Owing to the large differences in vulnerability indicators of different layers in the PCN, it is difficult to find the critical nodes, which have great impacts on all vulnerability indicators. Therefore, the goal of this study is to identify the critical nodes that have greater impacts on different layers, rather than nodes that have the greatest impact on a single layer. Therefore, the authors present a model to analyse the node in the PCNs from the physical topology, traffic distribution, and service importance distribution to calculate the node importance in the physical topology layer, the transport layer, and the service layer, respectively. Combined with a multi-layer critical nodes identification algorithm (MCNIA) proposed, the node critical degree is obtained so that it can identify the critical nodes in the PCNs. The vulnerability analyses of PCNs under critical nodes attacking prove that MCNIA can identify critical nodes in the PCNs precisely.

A minimum cut-set vulnerability analysis of power networks

In this paper, we study the vulnerability of the minimum cut-set (MCS) in power networks by assessing the effect of sequential attacks against the MCS and its corresponding effect on cascading failures triggering. Concepts of flow-networks and the DC power flow are integrated into the system model. Computational algorithms are developed to identify the target set, update power flow solutions, and maintain the system balance during network evolution. A vulnerability metric based on attack efficiency is proposed to compare the network responsible for different target selection. Case studies are used to compare MCS attacks with other network-based and electric-based attacks. The vulnerability of the MCS elements results higher than found for elements identified employing electrically extended measures.

A MDP-Based Vulnerability Analysis of Power Networks Considering Network Topology and Transmission Capacity

This paper aims to study the vulnerability of the network to sequential cascading failures attacks where the attack strategy integrates network theory and discounted reward with Markov decision process (MDP) in the target selection process. A control strategy is designed to maximize the attack's long-term expected reward while reducing the attack sequence duration. The attack model identifies the most suitable targets by prediction through a Markov process for predicting the propagation and consequences of the failure. The state transition probabilities through a hidden failure model embedded in an independent edge-dependent network evolution model is estimated. Value iteration algorithms are used to identify targets at every attack stage. Target selection is updated depending on network changes. The results provide an optimal attack strategy based on network congestion with maximum damage, considering congestion as a cascade propagation mechanism. Reward functions based on increasing congestion and immediate power loss are compared. Strategies designed with network congestion as the attack reward function produce more vulnerability of the network to sequential attacks.

Analysis of electrical network vulnerability using segmented cascading faults graph

To reveal the mechanism of fault propagation and temporal information between electrical network branches intuitively and vividly, we have proposed a fault chain-based cascading fault graph (CFG) that considers the topological, physical, and fault operational features from an overload mechanism perspective. The proposed CFG is used to construct metrics to identify vulnerable branches of an electrical network. Furthermore, because the vulnerable branch rankings change with the changing fault chain length, the ranking results’ change rules are investigated. As a result, the branch vulnerabilities’ characteristics are found to be different at different stages under sequential attacks. Inspired by the characteristics, the CFGs are divided into three sub-CFGs, based on load shedding threshold, to identify the vulnerable branches at different stages. The proposed method is used to identify the vulnerable branches of the IEEE 39- and 118-bus systems, and its effectiveness is validated by investigating load shedding of the systems under deliberate attacks.

Link failure in wheel type networks

Link residual closeness (LRC) is a new sensitive characteristic for network vulnerability analysis. LRC measures the vulnerability even when the removal of links does not disconnect the network. In this paper, the robustness of wheel type network topologies is modeled and analyzed via LRC in the face of possible link destruction.

Game Approach to Vulnerability Analysis of Evacuation Highway Networks

AbstractA hurricane can cause a lot of damage to highway networks and significantly affect the evacuation trip operations on the roads. A good understanding of network vulnerability can improve pre...

A Graph-Based Model for Transmission Network Vulnerability Analysis

A novel cascading faults graph (CFG) is constructed using the cascading failure model based on the continuous temperature evolution process of line. The CFG contains the temporal-spatial characteristics of fault chains, which is able to facilitate the cascading failure analysis in network science and statistics perspectives. For characterizing the vulnerability of lines and transmission networks, indices based on the CFG are proposed, where the line vulnerability is presented as propagation vulnerability. The characteristics of the proposed CFG and the mechanisms of cascades are revealed based on the properties of multiorder topology. Through simulations based on standard test and real systems, the validity of the proposed CFG and indices are verified. The results show that the vulnerability distribution of lines is highly heterogeneous, and the vulnerability of lines appears assortative correlation, which indicates that successive failures between two lines with high vulnerability are easy to occur in high probability. The results also show two vulnerability characteristics of transmission networks, the constancy of the vulnerability correlation and the vulnerability mitigation. The proposed method greatly reduces the number of upgrades of lines and can effectively prevent cascading failures.

Topological characteristics of metro networks based on transfer constraint

Based on graph theory, this paper presents a topological heterogeneity and vulnerability analysis of metro networks where transfer constraint is taken into account. The statistical measurements based on minimal transfer times are defined, such as betweenness, the shortest path and the average transfer times. Based on above, on the one hand, heterogeneity of network is analyzed by adopting entropy theory; on the other hand, travel efficiency and connectivity based on effective paths are proposed to measure the topological vulnerability of metro network. The feasibility and rationality of the methods described above are verified through the Beijing metro network. The results show that the topological analysis neglecting transfer constraint would underestimate the heterogeneity and vulnerability of the metro network: the topological heterogeneity and vulnerability of Beijing metro network with considering transfer constraint are smaller than without considering transfer constraint; in the case of considering transfer constraint, with the scale expansion of network, travel efficiency and convenience increase, and topological heterogeneity and vulnerability of network decrease, but the connectivity of metro network is more vulnerable to node-based attack.

Entropy of centrality values for topological vulnerability analysis of water distribution networks

Purpose The increased complexity of water distribution networks (WDNs) emphasizes the importance of studying the relationship between topology and vulnerability of these networks. However, the few existing studies on this subject measure the vulnerability at a specific location and ignore to quantify the vulnerability as a whole. The purpose of this paper is to fill this gap by extending the topological vulnerability analysis further to the global level. Design/methodology/approach This paper introduces a two-step procedure. In the first step, this work evaluates the degree of influence of a node by employing graph theory quantities. In the second step, information entropy is used as a tool to quantify the global vulnerability of WDNs. Findings The vulnerability analysis results showed that a network with uniformly distributed centrality values exhibits a lower drop in performance in the case of partial failure of its components and therefore is less vulnerable. In other words, the failure of a highly central node leads to a significant loss of performance in the network. Practical implications The vulnerability analysis method, developed in this work, provides a decision support tool to implement a cost-effective maintenance strategy, which relies on identifying and prioritizing the vulnerabilities, thereby reducing expenditures on maintenance activities. Originality/value By situating the research in the entropy theory context, for the first time, this paper demonstrates how heterogeneity and homogeneity of centrality values measured by the information entropy can be interpreted in terms of the network vulnerability.

Road Network Vulnerability Analysis: Case Study Considering Travel Demand and Accessibility Changes

AbstractRoad network vulnerability analysis is helpful in the improvement of vulnerable links with proper maintenance investments and management strategies. This paper addresses issues that have re...

Vulnerability Analysis of Intercity Multimode Transportation Networks; A Case Study of the Yangtze River Delta

The disruption of an intercity transportation network will seriously influence the economic activities and communication between cities. At the regional or national transportation network level, in most cases, there is no link redundancy because of the high construction cost of the extra transportation infrastructure, so the impacts caused by the disruption of regional intercity transportation networks should be paid more attention. This paper proposes an accessibility-based method to analyze the vulnerability of multimode transportation networks. The measurement captures the influence of the disruption of network components in terms of the travel cost, socio-economic impacts, and level of transportation service of cities, and besides the network vulnerability analysis, it can also be used in the analysis of city relationships and development status of cities, providing a reference direction of the sustainable development for the city cluster. Finally, with the rapid application of high-speed trains in China, a preliminary study of the public transportation network of the Yangtze River Delta (YRD) region is conducted to illustrate the applicability of the evaluating method. The results show that the proposed measurement can be used to analyze the transportation network from the perspective of socio-economic impacts and level of transportation service, providing a reference for network planners and managers.

Vulnerability Analysis of Urban Rail Transit Network within Multi-Modal Public Transport Networks

In terms of urban rail transit network vulnerability, most studies have focused on the network topology characteristics and travel cost changes after network incidents and analyzed rail transit network independently. The neglects of passenger flow distributions on the network and alternative public transport modes under rail network disruptions would either underestimate or overestimate the vulnerability of rail transit network, and thus lead to inaccurate results and decisions. This study presents an accessibility-based measurement for urban rail transit network vulnerability analysis and explicitly accounts for rail passenger flow characteristics, travel cost changes, and alternative transit modes. It is shown that the proposed approach is capable of measuring the consequences on rail network, and the advantages of the accessibility method are demonstrated and compared. The methodology is applied to the urban rail transit network of Shenzhen, China within a multi-modal public transport network. Results reveal that the consequences of disruptions on network accessibility are obviously different for stations with different passenger flow characteristics, and some undisrupted stations are found to be vulnerable under surrounding station failures. The proposed methodology offers reliable measurements on rail transit network vulnerability and implications for decision-making under rail network disruptions.

Is it Necessary to Relax the IID Assumptions in the Logsum-Based Accessibility Analysis?

Accessibility is an important link between transportation and land use. As a typical measure of accessibility, logsum or a utility-based measure has been widely used in project appraisal, urban transit accessibility evaluation, destination choice, and network vulnerability analysis. Since the logsum term is the log of the denominator of the choice probability expression, it inherits the independently and identically distributed (IID) assumptions of the classical multinomial logit (MNL) route choice model. This paper aims to explore whether the IID assumptions have a significant effect on the logsum-based accessibility analysis, given that accessibility analysis focuses at the origin-destination (O-D) level and zonal level (aggregate analysis) rather than at the route level (disaggregate analysis). We derive two new logsum terms for two representative extended logit stochastic user equilibrium (SUE) models, that is, the C-logit model for relaxing the independence assumption and the MNL model with scaling effect (MNLs) for relaxing the identically distributed assumption. The case analysis of a real network in Winnipeg, Canada shows that: (1) there does exist a difference in accessibility evaluation among the three logsum terms using the three route choice models; (2) relaxing the identically distributed assumption is more important than the independence assumption since the difference in accessibility evaluation between MNLs-logsum and MNL-logsum is larger than that between C-logit-logsum and MNL-logsum; (3) the difference in accessibility evaluation at the zonal level is smaller than that at the O-D level; and (4) the difference increases with the dispersion parameter.

Elevated Plasma microRNA-105-5p Level in Patients With Idiopathic Parkinson's Disease: A Potential Disease Biomarker.

Parkinson’s disease (PD) is the second most common neurodegenerative disease, which still lacks a biomarker to aid in diagnosis and to differentiate diagnosis at the early stage of the disease. microRNAs (miRNAs) are small and evolutionary conserved non-coding RNAs that are involved in post-transcriptional gene regulation. Several miRNAs have been proposed as potential biomarkers in several diseases. In the present study, we screened miRNAs using a network vulnerability analysis, to evaluate their potential as PD biomarkers. We first extracted miRNAs that were differentially expressed between PD and healthy controls (HC) samples. Then we constructed the PD-specific miRNA-mRNA network and screened miRNA biomarkers using a new bioinformatics model. With this model, we identified miR-105-5p as a putative biomarker for PD. Moreover, we measured miR-105-5p levels in the plasma of patients with idiopathic PD (IPD) (n = 319), neurological disease controls (NDC, n = 305) and HC (n = 273) using reverse transcription real-time quantitative PCR (RT-qPCR). Our data clearly demonstrated that the plasma miR-105-5p level in IPD patients was significantly higher than those of HC (251%, p < 0.001) and NDC (347%, p < 0.001). There was no significant difference in miR-105-5p expression between IPD patients with or without anti-PD medications. Interestingly, we found that the plasma miR-105-5p expression level may be able to differentiate IPD from parkinsonian syndrome, essential tremor and other neurodegenerative diseases. We believe that a change in the plasma miR-105-5p level is a potential biomarker for IPD.

Complex Network-Based Cascading Faults Graph for the Analysis of Transmission Network Vulnerability

Transmission network vulnerability (TNV) assessment is a key issue in power systems to identify the vulnerable components against accidents or malicious threats. Recently, constructing the topological vulnerability indices, particularly extended topological indices, is a popular method to evaluate the network vulnerability. However, the topological vulnerability indices cannot reveal the mechanism of fault propagation. To overcome the shortcomings, this paper proposes a new method to assess the TNV through the cascading faults graph (CFG) based on fault chains, which is a statistical graph that comprehensively considers the physical, operational, and structural features of electrical networks. Based on the complex network theory (CNT), the scale-free properties of the CFG are revealed through simulations on various transmission networks by corresponding the degree distribution of the CFG; then, the model constancy of the CFG is analyzed. Resorting to the CFG, a set of indices from the CNT is used to identify the vulnerable branches of transmission networks. Illustrative applications are applied to the IEEE 39-bus and 118-bus test systems to demonstrate the effectiveness of the proposed method.

Coupling Natural Hazard Estimates with Road Network Analysis to Assess Vulnerability and Risk: Case Study of Freetown (Sierra Leone)

Many of the world’s most disaster-prone cities are also the most difficult to model and plan. Their high vulnerability to natural hazards is often defined by low levels of economic resources, data scarcity, and limited professional expertise. As the frequency and severity of natural disasters threaten to increase with climate change, and as cities sprawl and densify in hazardous areas, better decision-making tools are needed to mitigate the effects of near- and long-term extreme events. We use mostly public data from landslide and flooding events in 2017 in Freetown, Sierra Leone to simulate the events’ impact on transportation infrastructure and continue to simulate alternative high-risk disasters. From this, we propose a replicable framework that combines natural hazard estimates with road network vulnerability analysis for data-scarce environments. Freetown’s most central road intersections and transects are identified, particularly those that are both prone to serviceability loss due to natural hazard and whose disruption would cause the most severe immediate consequences on the entire road supply in terms of connectivity. Variations in possible road use are also tested in areas with potential road improvements, pointing to opportunities to harden infrastructure or reinforce redundancy in strategic transects of the road network. This method furthers network science’s contributions to transportation resilience under hydrometeorological hazard and climate change threats with the goal of informing investments and improving decision-making on transportation infrastructure in data-scarce environments.

Wi-Fi Network Vulnerability Analysis and Risk Assessment in Lebanon

The aim of this paper is to analyze the Wi-Fi network vulnerabilities and risk assessment in Lebanon in order to raise awareness to the Lebanese public by informing them of threats that occur on the network and their impact. The analysis is done by first performing a wardrive; this involves capturing the network access points in several Lebanese regions with the use of two programs: Acrylic Wi-Fi Professional™, and Kismet™, as well as connecting an Alfa Network Antenna to the device in order to increase the range of capture. The data collected are stored in a database where they are processed in order to generate a statistics report of the security and risk level of the network in selected cities. Moreover, a custom survey of the basic knowledge of networking and security is publicly distributed.

Analysis of incident costs in a vulnerable sparse rail network – Description and Iran case study

Enhancing the productivity of the national railway systems is now one of the main concerns of transportation authorities in Iran. In this regard, one of the strategies devised by Iran's Railway Company is to increase the robustness of the system against incidents. The paper describes the state of the network and discusses that human errors are the main cause imposing significant costs. This is followed by the development of a method for the network vulnerability analysis of sparse interurban rail networks considering transportation of various goods as well as passengers. The paper examines the effects of an incident where consequent effects are substantial and propagate functionally throughout the network, to take the network closure duration into account, and to tie together both additional costs of transporting passengers/goods (due to prolongation of routes) and unsatisfied demand. The purpose is to find the most important links of the network, where failure results in serious consequences. Passenger unsatisfied demand, freight unsatisfied demand, and monetary costs are considered as the consequences of network disruptions, and a multi-objective optimization programming approach is designed to generate solutions based on these criteria.