Network Vulnerability Assessment Research Articles

Identification and Evaluation of Vulnerable Links in a Distribution Network with Renewable Energy Source Based on Minimum Discriminant Information

With the increase in the proportion of photovoltaic and wind power access, the scale and form of distribution networks are becoming more and more complex. The traditional single distribution network vulnerability assessment method is difficult to use to identify the vulnerable links in the distribution network. Therefore, this paper proposes a method for identifying and evaluating vulnerable links in distribution networks based on minimum discriminant information. First, considering the influence of distributed grid connection, an improved probabilistic power flow calculation method is proposed, which improves the calculation efficiency and accuracy. Second, considering the correlation degree, transmission capacity, and voltage stability of branches in the distribution network, the identification index of vulnerable lines is defined. Based on power quality and operating state, the identification index of vulnerable nodes in a distribution network is defined. Finally, based on the indicators of vulnerable nodes and vulnerable lines, the vulnerable links in the distribution network are comprehensively evaluated based on the principle of minimum discriminant information, and the vulnerable links of the entire distribution network are evaluated according to different degrees of vulnerability. The rationality and effectiveness of the proposed method are verified via an example analysis of actual power grid data.

Power Network Vulnerability Assessment Based on Electric Power System Cyber Security Simulation Platform

Abstract This paper introduces a modeling approach for the integration of electric power and information networks, along with the creation of an electric power system network simulation platform. The system, achieved through the deep integration of computation, communication, and control technologies, offers new insights into power grid intelligence. The paper discusses the concept of power system vulnerability and presents a modeling scheme that effectively simulates and analyzes power systems. Using the Analytic Hierarchy Process (AHP), the impact of cyber attacks under different load conditions is evaluated, revealing that attacks have more severe consequences under heavy loads. In conclusion, this research contributes to enhancing the security and stability of power systems.

Liner shipping network vulnerability to component disruptions: A China-Europe container flow analysis

This study conducts a vulnerability analysis of the China-Europe liner shipping network, including all relevant liner shipping services and China Railway Express services. The standard multicommodity network flow model is extended to assess network vulnerability and the relative importance of ports and services by simulating container flow redistribution in various disruption scenarios. The results show the network is highly resilient to both individual port disruptions and individual service disruptions. Additionally, the distinct shipping network structures of the three shipping alliances result in varying degrees of vulnerability, highlighting the significance of diversified market suppliers in enhancing the overall network resilience. Specifically, the Ocean Alliance is vital in facilitating China-Europe trade, especially along the Mediterranean route, and is more resilient to individual port disruptions.

Enhancing Electrical Network Vulnerability Assessment with Machine Learning and Deep Learning Techniques

This research utilizes advanced machine learning techniques to evaluate node vul- nerability in power grid networks. Utilizing the SciGRID and GridKit datasets, con- sisting of 479, 16,167 nodes and 765, 20,539 edges respectively, the study employs K-nearest neighbor and median imputation methods to address missing data. Cen- trality metrics are integrated into a single comprehensive score for assessing node criticality, categorizing nodes into four centrality levels informative of vulnerability. This categorization informs the use of traditional machine learning (including XG- Boost, SVM, Multilayer Perceptron) and Graph Neural Networks in the analysis. The study not only benchmarks the capabilities of these models in network analy- sis but also explores their potential in identifying critical nodes using features be- yond centrality metrics alone, enhancing their applicability in real-world scenarios. The research addresses a significant gap in effectively assessing the vulnerability of electrical networks, marked by isolated use of traditional centrality metrics and a lack of integration between these combined metrics with both tradiational and ad- vanced machine learning models. The study integrates various centrality measures into a comprehensive metric and advocates for the application of advanced ma- chine learning models, particularly GNNs. It underscores the need for larger and more complex datasets to unlock the full potential of GNNs in network vulnerabil- ity assessments. By comparing the performance of GNN models with traditional machine learning approaches across datasets of different sizes and complexities, the study provides insights into optimizing model selection for network analysis, thereby contributing significantly to the field of network vulnerability assessment.

A Literature Survey on System Security and Network Vulnerability Assessment

This abstract outlines a detailed study on improving network security through vulnerability assessment and penetration testing, using tools like Nessus, Nmap, Metasploit, on the Kali Linux OS. The methodology involves identifying vulnerabilities, conducting simulated attacks, and developing a bash script to address false positives from Nessus scans. The research aims to enhance network security, provide practical recommendations, and contribute to cybersecurity practices, with a focus on both internal and external network security. Keywords: Vulnerability Assessment, Pentest, Nessus, Metasploit, Kali Linux

On the dynamic vulnerability of an urban rail transit system and the impact of human mobility

Urban rail transit (URT) plays a pivotal role in facilitating human mobility within urban environments. It is significant to understand its vulnerability, i.e., the variation in capacity and demand when confronted with unexpected events, particularly operational disruptions. Although the network topology is generally fixed, the hourly-changing travel demand greatly impacts the actual vulnerability of a URT system. Unfortunately, few existing studies consider the combined influence of dynamic travel demand and network topology. To fill the gap, this paper proposes a network vulnerability assessment method with the joint consideration of static network topology and dynamic travel demand. This method includes a defined reasonable path, an accessibility-based identification of station importance with time-varying passenger demand, and a new dynamic vulnerability delay index considering affected travel demand. An empirical analysis was carried out by taking the URT system of Beijing, China as an example, and the impact of the more realistic multiple consecutive station failures in a URT system is also examined. Results show that the distribution of high-importance stations indeed varies with the time of day, affected by both static topology and hourly-changing passenger flow. When the disturbance of operation delay occurs, the impact of high-importance stations on the network vulnerability changes nonlinearly with the increase of delayed travel demand. Some stations that serve as bridges and are visited by large passenger flows have the greatest impact on network vulnerability. Network performance degradation is obviously segmented and stratified in the case of interval continuous failure. The disruption between different lines is the main cause of network performance degradation, and some high-importance stations within the lines act as catalysts to accelerate the performance degradation. The proposed method not only offers a valuable reference for quantifying network vulnerability arising from fluctuations in passenger mobility but also introduces a novel vulnerability evaluation index to the URT system.

Shipping network vulnerability assessment integrated with geographical locations

In maritime transport, ports undertake the routes and markets of surrounding ports after they fail during emergencies. The transfer and replacement of routes are subject to port-hinterland relationships, spatial agglomeration, and geopolitical environments, which are realistic expressions of port location from the perspectives of economy, geography, and geopolitics. However, the irreplaceability of ports influenced by geographic location has been ignored in previous evaluations of network vulnerability, which has exaggerated the network vulnerability. To address the aforementioned problems, an irreplaceability model based on the port’s geographical location was constructed. This model systematically simulates the changing trend of shipping network vulnerability and reveals the damage effect of the shipping network under the influence of emergencies to bring the vulnerability assessment results of shipping networks closer to reality.

Detection and Prevention of Cyber Defense Attacks using Machine Learning Algorithms

Recent advancements in computing power, memory capacities, and connectivity have led to a corresponding surge in the utilization of big data, online platforms' prevalence, and machine learning's sophistication. Concerns regarding safety and the need for state-of-the-art security tools and methods to counter evolving cybercrime are amplified by the swift digitization of the world. This study investigates defensive and offensive applications of machine learning in cybersecurity. Additionally, it explores potential strategies to mitigate cyberattacks on machine learning models. The focus is on how machine learning facilitates cyberattacks, including developing intelligent botnets, advanced phishing using spear techniques, and deploying stealthy malware. Furthermore, the paper highlights the significance of artificial intelligence in digital safety, emphasizing its role in malware analysis, network vulnerability assessment, and threat prediction.

Dynamic assessment of postdisaster road network vulnerability using crowdsourced traffic data

Accurate identification of the spatial and temporal dynamic impacts of disasters on road networks is crucial for making informed decisions regarding postdisaster emergency response and recovery. In this paper, we propose a multidimensional vulnerability assessment framework that utilizes time series crowdsourced data. The framework enables a spatiotemporal analysis of traffic flows at the link level, allowing us to identify the primary factors that contribute to vulnerability after a disaster. The results show that there is a significant variation in link vulnerability over time, with the overall network gradually returning to a normal state. We also observe a global correlation in link vulnerability across the spatial aspect, although the autocorrelation effect is only present among directly contiguous links. Furthermore, metrics affecting link vulnerability varied during different periods of response. Graph-based metrics of road network structure, population density, and road type are key factors influencing postearthquake vulnerability.

Addressing the Dilemma of a “Crisis within a crisis”: Exploring the Penetration Testing challenges in a Mobile Field Hospital Setting

This paper focuses on evaluating the security challenges faced by mobile field hospitals, which play a crucial role in emergency response and disaster management in remote and austere environments. The authors conducted a penetration test using the Open Source Security Testing Methodology Manual (OSSTMM) framework to assess the security posture of a mobile field hospital. The methodology employed in the study included a combination of automated and manual techniques such as network scanning, vulnerability assessments, social engineering, and exploitation. The penetration test revealed several security vulnerabilities in the mobile field hospital, including weak passwords, unpatched software, and inadequate network segmentation. Additionally, the study identified vulnerabilities in the hospital's medical devices and equipment, posing a risk of cyber-attacks that could disrupt operations and compromise patient safety. The results underscore the importance of implementing enhanced security measures in mobile field hospitals to mitigate cyber threats and ensure the uninterrupted functioning of medical operations during emergencies and disasters. This study provides a comprehensive analysis of the mobile field hospital's security posture using the OSSTMM framework and emphasizes the urgent need for improved security practices in such settings. Keywords: Cybersecurity, Healthcare, Penetration Testing, Mobile Field Hospitals, Data, Emergency Response, Open Source, Security Testing, Ethical Hacking, Security Frameworks, Proceedings Citation Format Ahmed, N.B., Daclin, N., Olivaux, M. & 4usserre, G. (2023): Addressing the Dilemma of a “Crisis within a crisis”: Exploring the Penetration Testing challenges in a Mobile Field Hospital Setting. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 67-80 https://www.csean.org.ng/. dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P9

Identification of Network Vulnerabilities through IISRA Framework

Abstract: As the globe transitions to complicated networks and as we move towards digitization, its worth is rising daily. Working in an organization across a network and the internet creates vulnerabilities. As is well known, data is a crucial component of every organization and must be safeguarded from threats. The attackers' job is to try and take advantage of the networks by using these weaknesses. When organizations use the Internet, intranets, and related technologies more frequently, system security becomes one of the key considerations. Network security protects computer systems from unauthorized threats and breaches, which lowers the likelihood that confidential information may be stolen. The organization will feel more secure if these vulnerabilities are closed up in the systems and network well in advance of an attack. The availability of numerous technologies for network vulnerability assessment enables organizations to thwart potential attacks. In this research paper, we have developed an Integrated Information Security Risk Assessment (IISRA) Framework for identification of Network Vulnerabilities. We have implemented this IISRA Framework on real computing environment of an organization.

A global assessment of national road network vulnerability

Every country relies on a well-functioning road system. However, we do not have a clear understanding yet of the vulnerability of each of these road networks to different forms of disruption. In this study, we aim to better understand how road networks are affected by different disruptive events, to identify hotspots of road network vulnerabilities, and to better target where and what type of future investments can be made to develop more resilient networks. To do so, we developed a fully open-source modelling framework to expose over 200 country road systems across the world to random, local, and targeted disruption schemes. For each country, we assessed the impact of such disruptions on intra-country travel activities and regional accessibility. The results highlight the vulnerability of road systems in mountainous and small-island countries owing to the limited availability of alternative routes. Additionally, we find that, on average, low-income countries experience a collapse of road-system services with much fewer disruptions, relative to high-income countries, due to the lack of redundancy in their systems. While the value of goods and services disrupted may be higher in wealthier countries, the results highlight that from an equity perspective, transport infrastructure investments are more desired in low-income country networks.

Mapping health vulnerability to short-term summer heat exposure based on a directional interaction network: Hotspots and coping strategies

Health risk resulting from non-optimal temperature exposure, referred to as “systematic risk”, has been a sustainable-development challenge in the context of global warming. Previous studies have recognized interactions between and among system components while assessing the vulnerability to climate change, but have left open the question of indicator directional interactions. The question is important, not least because indicator directional association analysis provides guidance to address climate risks by revealing the key nodes and pathways. The purpose of this work was to assess health vulnerability to short-term summer heat exposure based on a directional interaction network. Bayesian network model and network analysis were used to conduct a directional interaction network. Using indicator directional associations as weights, a weighted technique for the order of preference by similarity to ideal solution method was then proposed to assess heat-related health vulnerability. Finally, hotspots and coping strategies were explored based on the directional interaction network and health vulnerability assessments. The results showed that (1) indicator directional interactions were revealed in the health vulnerability framework, and the interactions differed between northern and southern China; (2) there was a dramatic spatial imbalance of health vulnerability in China, with the Beijing-Tianjin-Hebei Region and the Yangtze River Basin identified as hotspots; (3) particulate matter and ozone were recognized as priority indicators in the most vulnerable cities of northern China, while summer heat exposure level and variation were priority indicators in southern China; and (4) adaptive capacity could alter the extent of risk; thus, mitigation and adaptation should be implemented in an integrated way. Our study has important implications for strengthening the theoretical basis for the vulnerability assessment framework by providing indicator directional associations and for guiding policy design in dealing with heat-related health vulnerability in China.

A Review on Attack Graph Analysis for IoT Vulnerability Assessment: Challenges, Open Issues, and Future Directions

Vulnerability assessment in industrial IoT networks is critical due to the evolving nature of the domain and the increasing complexity of security threats. This study aims to address the existing gaps in the literature by conducting a comprehensive survey on the use of attack graphs for vulnerability assessment in IoT networks. Attack graphs serve as a valuable cybersecurity tool for modeling and analyzing potential attack scenarios on systems, networks, or applications. The survey covers the research conducted between 2016 and 2021(34 peer-reviewed journal articles and 28 conference papers), identifying and categorizing the main methodologies and technologies employed in generating and analyzing attack graphs. In this review, core modeling techniques for IoT vulnerability assessment are highlighted, such as Markov Decision Processes (MDP), Feature Pyramid Networks (FPN), K-means clustering, and logistic regression models, along with other techniques involving genetic algorithms like fast-forward (FF), contingent fast-forwards (CFF), advanced reinforcement-learning algorithms, and HARMs models. The evaluation of the performance of these attack graph models using IoT networks or devices as case studies is also emphasized. This survey provides valuable insights into the state-of-the-art attack graph techniques for IoT network vulnerability assessment, identifying various applications, performances, research opportunities, and challenges. As a reference source, it serves to inform academicians and practitioners interested in leveraging attack graphs for IoT network vulnerability assessment and guides future research directions in this area.

Multi-level time-variant vulnerability assessment of deteriorating bridge networks with structural condition records

Bridges are critical to ensuring access to all parts of the transportation network, and they are also the most vulnerable infrastructures. This study proposes a multi-level vulnerability assessment framework that considers structural deterioration effects and network characteristics. The condition deterioration of regional bridges is predicted with the machine learning model, which is trained with years of regional inspection data. The failure of a bridge network is converted into a Bayesian network model that takes bridge failure probability into account while assessing network vulnerability. The service performance of bridge networks is assessed with the multi-level time-variant vulnerability analysis method. The global and local network vulnerabilities are revealed through the edge-level, path-level, and network-level analysis. The proposed multi-level vulnerability assessment method is validated with a real regional bridge network. The trained U-Net model achieves high prediction performance for predicting the future condition of regional bridges. The service performance of bridge networks is comprehensively assessed with the proposed multi-level time-variant vulnerability analysis method. The corresponding results could provide a reference for the safety of managing regional bridges from a network-level viewpoint.

CRUSOE: A toolset for cyber situational awareness and decision support in incident handling

The growing size and complexity of today’s computer network make it hard to achieve and maintain so-called cyber situational awareness, i.e., the ability to perceive and comprehend the cyber environment and be able to project the situation in the near future. Namely, the personnel of cybersecurity incident response teams or security operation centers should be aware of the security situation in the network to effectively prevent or mitigate cyber attacks and avoid mistakes in the process. In this paper, we present a toolset for achieving cyber situational awareness in a large and heterogeneous environment. Our goal is to support cybersecurity teams in iterating through the OODA loop (Observe, Orient, Decide, Act). We designed tools to help the operator make informed decisions in incident handling and response for each phase of the cycle. The Observe phase builds on common tools for active and passive network monitoring and vulnerability assessment. In the Orient phase, the data on the network are structured and presented in a comprehensible and visually appealing manner. The Decide phase opens opportunities for decision-support systems, in our case, a recommender system that suggests the most resilient configuration of the critical infrastructure. Finally, the Act phase is supported by a service that orchestrates network security tools and allows for prompt mitigation actions. Finally, we present lessons learned from the deployment of the toolset in the campus network and the results of a user evaluation study.

Assessment of the vulnerability of road networks to urban waterlogging based on a coupled hydrodynamic model

Waterlogging events in urban areas are becoming increasingly more frequent, which has led to tremendous economic losses. Urban road networks have also suffered heavy interference and destruction due to this type of disaster, and the precise assessment of road network vulnerability is an effective measure by which to reduce losses. Therefore, in this study, an analytical framework for the assessment of the vulnerability of road networks to urban waterlogging was constructed by using a coupled hydrodynamic model. The rainfall patterns in the study area were detected, and their impacts on vulnerability are discussed. The results show that rainfall events with unimodal, bimodal, and uniform shape patterns respectively account for 68.08%, 31.46%, and 0.47% of the total number of events considered in this study. The coupled hydrodynamic model used in this study is found to have satisfactory applicability for waterlogging simulation. Rainfall with a unimodal shape is found to have the greatest impact on the vulnerability of road networks, while that with a uniform shape has the least. Among the unimodal shapes, Mode II (late peak) and Mode III (middle peak) are the most dangerous.

Dynamic Assessment of Road Network Vulnerability Based on Cell Transmission Model

The road network maintaining stability is critical for guaranteeing urban traffic function. Therefore, the vulnerable links need to be identified accurately. Previous vulnerability research under static condition compared the operating states of the old equilibrium before the event and the new equilibrium after the event to assess vulnerability ignoring the dynamic variation process. Does road network vulnerability change over time? This paper combines the vulnerability assessment with the traffic flow evolution process, exploring the road network vulnerability evaluation from the perspective of time dimension. More accurate identification and evaluation of vulnerable nodes and links can help to strengthen the ability of road network resisting disturbances. A modified dynamic traffic assignment (DTA) model is established for dynamic path selection (reselect the shortest path at the end of each link) based on the dynamic user optimal (DUO) principle. A modified cell transmission model is established to simulate the traffic flow evolution processes. The cumulative and time-varying index of vulnerability assessment is established from the viewpoint of traveler’s time loss. Then the road network vulnerability assessment combined the traffic flow model with the vulnerability index. The road network vulnerability assessment of Bao’an Central District of Shenzhen, China, reveals that road network vulnerability does contain a dynamic process, and vulnerable links in each phase can be exactly identified by the model. Results showed that the road network would have a large vulnerability during the disordered phase when the main road fails. Therefore, prioritizing the smooth flow of main roads can weaken the impact of road network vulnerability exposure.

Topological-Based Measures with Flow Attributes to Identify Critical Links in a Transportation Network

An important part of transportation network vulnerability analysis is identifying critical links where failure may lead to severe consequences, and the potential of such incidents cannot be considered negligible. Existing transportation network vulnerability assessment can be categorized as topological, or traffic based. Topological-based assessment identifies the most critical components in the network by considering network structure and connectivity. Traffic-based assessment identifies the most critical components in the network by full-scan analysis and takes into consideration effects of link failures to traffic flow assignment. The former approach does not consider traffic flow dynamics and fails to capture the non-linearity performance function of transport systems while the latter, even though accurate and robust, requires significant computational power and time and may not always be feasible for real life size networks. The primary objective of this paper is to propose new link criticality measures and evaluate their accuracy for transportation network vulnerability assessment. These measures combine characteristics of traffic equilibrium and network topology to balance accuracy and computational complexity. Nine measures are proposed, and their accuracy is compared with three existing traffic-based measures using three case study transportation networks from the literature. Results indicate that three of the proposed measures show strong correlation to the three traffic-based measures while requiring significantly less computational power and time.

Research on network vulnerability assessment based on attack graph and security metrics

In recent years, the information security environment is undergoing great changes. On one hand, with the development of technology and the advancement of network integration, network involves in a larger and larger scale, and its structure is becoming more and more complex. On the other hand, the development of attack technology has also taken a qualitative leap. The ability of violent cracking and the scale of botnets are no longer the primary factors that determine the attack effect. On the contrary, attackers are more likely to adopt information-driven complex combination attacks with clear attack targets. These changes have brought great challenges to network security defense. Therefore, from the perspective of building a secure network environment, this paper studies network vulnerability assessment in the field of computer network security, and proposes a method of network vulnerability assessment based on attack graph and security metrics. This method models and constructs the attack graph for the possible network attack behaviors. Besides, taking it as the analysis model, the risk assessment technology is taken to determine the indicators and calculating methods of security metrics so as to realize the evaluation of network vulnerability. Finally, the discussion was conducted based on the results, and a follow-up study was suggested.