Critical Infrastructure Networks Research Articles

Investigating the Secrets, New Challenges, and Best Forensic Methods for Securing Critical Infrastructure Networks

As critical infrastructure networks become more interconnected and digitalized, they confront increased cyber threats fueled by the growing adoption of digitalized working and general operation methods around the world. This research delves into the complex topic of critical infrastructure network security by examining the hidden challenges and best forensic techniques used to safeguard these crucial systems. The study utilizes a comprehensive data collection approach that integrates an experiment and a case study to provide an in-depth understanding of this essential subject. It assesses the efficacy of various digital forensic procedures customized for critical infrastructure network protection by using meticulously designed experiments within controlled simulated environments. The findings highlight the wide range of challenges and threats that organizations tasked with maintaining and securing these networks encounter. The case study illuminates how forensic practices can be used in incident response and recovery situations. The results highlight the significance of a diversified approach to safeguarding critical infrastructure networks. They emphasize the need for modern methods and practices, such as blockchain technology and Artificial intelligence, by analyzing findings from the experiment and the case study.

Cyber Security; Etiology and Importance

Background: Cyber Security is to protect online data and software from cyber threats. These cyberattacks are typically intended to gain access to, change, or delete sensitive information; extort money from users; or disrupt regular corporate activities. It is difficult to keep up a regular follow up with new technologies, so it is necessary to keep the important data safe from cyber threats. There are many types of cyber threats, malware, ransomware, social engineering, phishing etc. To prevent cyber-attacks one can, use password manager tools like LastPass and others. People also use two factor authentication for double security on their accounts. Methods: Boards such as the National Institute of Standards and Technology (NIST) are developing frameworks to assist firms in understanding their security risks, improving cybersecurity procedures, and preventing cyber assaults. The fight against cybercrimes and attack, organizations needed a strong base there are 5 types of cyber securities: Critical Infrastructure Security, application security, network security, cloud security and (IoT) Security. In the modern time the US is highly based on computers and on different software, so it is really important for the US to be more conscious about the security as they get many threats almost every day for hacking their data and accounts. Results and Conclusion: Nowadays, even small businesses rarely recover their loss from the cyber-attacks and many back-off from continuing their businesses after being target of hackers. The first cybercrime attack was recorded in 1988 by a graduate student. Now that large companies and even small businesses are aware of cyber-attacks, they try their best to take every precaution to prevent hacking with double security and password manager tools.

Data for critical infrastructure network modelling of natural hazard impacts: Needs and influence on model characteristics

Natural hazards impact interdependent infrastructure networks that keep modern society functional. While a variety of modelling approaches are available to represent critical infrastructure networks (CINs) on different scales and analyse the impacts of natural hazards, a recurring challenge for all modelling approaches is the availability and accessibility of sufficiently high-quality input and validation data. The resulting data gaps often require modellers to assume specific technical parameters, functional relationships, and system behaviours. In other cases, expert knowledge from one sector is extrapolated to other sectoral structures or even cross-sectorally applied to fill data gaps. The uncertainties introduced by these assumptions and extrapolations and their influence on the quality of modelling outcomes are often poorly understood and difficult to capture, thereby eroding the reliability of these models to guide resilience enhancements. Additionally, ways of overcoming the data availability challenges in CIN modelling, with respect to each modelling purpose, remain an open question. To address these challenges, a generic modelling workflow is derived from existing modelling approaches to examine model definition and validations, as well as the six CIN modelling stages, including mapping of infrastructure assets, quantification of dependencies, assessment of natural hazard impacts, response & recovery, quantification of CI services, and adaptation measures. The data requirements of each stage were systematically defined, and the literature on potential sources was reviewed to enhance data collection and raise awareness of potential pitfalls. The application of the derived workflow funnels into a framework to assess data availability challenges. This is shown through three case studies, taking into account their different modelling purposes: hazard hotspot assessments, hazard risk management, and sectoral adaptation. Based on the three model purpose types provided, a framework is suggested to explore the implications of data scarcity for certain data types, as well as their reasons and consequences for CIN model reliability. Finally, a discussion on overcoming the challenges of data scarcity is presented.

Weather-related fragility modelling of critical infrastructure: a power and railway case study

Climate change has led to more frequent and severe extreme weather events, which impact critical infrastructure networks such as railway and power systems. Although infrastructure networks are interdependent, the analysis to understand the impact of weather events on infrastructure systems is usually performed in sector-specific silos. A methodology to examine how the same weather events affect different infrastructure sectors is presented, in order to understand cross-sectoral impact of extreme weather for interconnected regional infrastructure. Fragility modelling was used to examine the impact of temperature and rainfall on power and rail system failures using the West Midlands (in the UK) as a case study. The results demonstrated that the impact of temperature was broadly consistent across both infrastructure networks, showing less impact until specific upper and lower thresholds are passed; these thresholds were found to be similar for the different infrastructure networks evaluated, but railway infrastructure was found to be impacted more by lower temperatures. A growing correlation between the number of faults on power and railway systems was also found for both rainfall and temperature, indicating the value in coordinating preparation and planning efforts. For infrastructure operators and owners, regional resilience forums and other decision makers, this study provides an approach to assess the regional impact of extreme weather across multiple infrastructure sectors. The results give useful insights to inform the allocation of resources in response to extreme weather events.

Approaching the challenge of multi-phase, multi-hazard volcanic impact assessment through the lens of systemic risk: application to Taranaki Mounga

AbstractEffective volcanic impact and risk assessment underpins effective volcanic disaster risk management. Yet contemporary volcanic risk assessments face a number of challenges, including delineating hazard and impact sequences, and identifying and quantifying systemic risks. A more holistic approach to impact assessment is required, which incorporates the complex, multi-hazard nature of volcanic eruptions and the dynamic nature of vulnerability before, during and after a volcanic event. Addressing this need requires a multidisciplinary, integrated approach, involving scientists and stakeholders to co-develop decision-support tools that are scientifically credible and operationally relevant to provide a foundation for robust, evidence-based risk reduction decisions. This study presents a dynamic, longitudinal impact assessment framework for multi-phase, multi-hazard volcanic events and applies the framework to interdependent critical infrastructure networks in the Taranaki region of Aotearoa New Zealand, where Taranaki Mounga volcano has a high likelihood of producing a multi-phase explosive eruption within the next 50 years. In the framework, multi-phase scenarios temporally alternate multi-hazard footprints with risk reduction opportunities. Thus, direct and cascading impacts and any risk management actions carry through to the next phase of activity. The framework forms a testbed for more targeted mitigation and response planning and allows the investigation of optimal intervention timing for mitigation strategies during an evolving eruption. Using ‘risk management’ scenarios, we find the timing of mitigation intervention to be crucial in reducing disaster losses associated with volcanic activity. This is particularly apparent in indirect, systemic losses that cascade from direct damage to infrastructure assets. This novel, dynamic impact assessment approach addresses the increasing end-user need for impact-based decision-support tools that inform robust response and resilience planning.

Game-theoretic algorithm for interdependent infrastructure network restoration in a decentralized environment.

Having reliable interdependent infrastructure networks is vital for well-being of a safe and productive society. Systems are vulnerable to failure or performance loss due to their interdependence among various networks, as each failure can propagate through the whole system. Although the conventional view has concentrated on optimizing the restoration of critical interdependent infrastructure networks using a centralized approach, having a lone actor as a decision-maker in the system is substantially different from the actual restoration decision environment, wherein infrastructure utilities make their own decisions about how to restore their network service. In a decentralized environment, the definition of whole system optimality does not apply as each decision-maker's interest may not converge with the others. Subsequently, this results in each decision-maker developing its own reward functions. Therefore, in this study, we address the concern of having multiple decision-makers with various payoff functions in interdependent networks by proposing a decentralized game theory algorithm for finding Nash equilibria solutions for network restoration in postdisaster situations.

Heterogeneous cyber-physical network coexistence through interference contribution rate and uplink power control algorithm (ICR-UPCA) in 6G edge cells

Optimizing power control for interference mitigation at the network cell edge is pivotal in enhancing capacity within a heterogeneous cyber-physical infrastructure, such as smart cities, manufacturing, healthcare, energy grids, transportation, and agriculture, among others. In this paper, we consider the intricate dynamics of Internet of Things (IoT) 5/6G edge users, with a particular focus on the Interference Contribution Rate (ICR), where macro and femtocells are critical network infrastructures. Existing approaches has drawbacks such as computational complexity, overhead, and co-channel interference, among others. However, to fully address interference challenges from the coexistence of diverse network hierarchies, preserving the Quality of Service for femtocell users is prioritized. The paper concurrently enhances the handoff mechanism of cell edge users in the macro cell network. A two-tier heterogeneous network (HetNet) is utilized to initially assess the contribution of edge user equipment (UE) to interference levels during its active state while quantifying it as ICR. Game theory is used to formulate a cohesive model for the coexistence of macro cell (MUE) and femtocell users (FUE). ICR-based uplink power control and reference signal received quality (RSRQ)-based handoff algorithms are deployed to regulate interference levels and enhance the Signal-to-Interference-Noise Ratio (SINR) of the MUE at the cell edge. This is achieved through coordinated transmit power adjustments by both user types. Results indicate a 6.67 % channel capacity loss (interference tolerance) by the FUE, leading to a 12.5 % improvement, translating to approximately 4 Mbps and 1 Mbps channel enhancements, respectively. The MUE and FUE can effectively coordinate power control with minimal overhead, accepting compromises in network channel quality. This approach facilitates improved MUE data access rates while ensuring the preservation of FUE. We show that interference is successfully mitigated through power control in heterogeneous networks with lower computational complexity.

Analyzing Autoencoder-Based Intrusion Detection System Performance

The rise in cyberattacks targeting critical network infrastructure has spurred an increased emphasis on the development of robust cybersecurity measures. In this context, there is a growing exploration of effective Intrusion Detection Systems (IDS) that leverage Machine Learning (ML) and Deep Learning (DL), with a particular emphasis on autoencoders. Recognizing the pressing need to mitigate cyber threats, our study underscores the crucial importance of advancing these methodologies. Our study aims to identify the optimal architecture for an Intrusion Detection System (IDS) based on autoencoders, with a specific focus on configuring the number of hidden layers. To achieve this objective, we designed four distinct sub-models, each featuring a different number of hidden layers: Test 1 (one hidden layer), Test 2 (two hidden layers), Test 3 (three hidden layers), and Test 4 (four hidden layers).We subjected our models to rigorous training and testing, maintaining consistent neuron counts of 30 and 60. The outcomes of our experimental study reveal that the model with a single hidden layer consistently outperformed its counterparts, achieving an accuracy of 95.11% for NSL-KDD and an impressive 98.6% for CIC-IDS2017. The findings of our study indicate that our proposed system is viable for implementation on critical network infrastructure as a proactive measure against cyber-attacks.

Evaluation and Selection of a Cybersecurity Platform ─ Case of the Power Sector in India

Maintaining interconnected infrastructures such as transportation, communication, power grids, and pipeline networks is paramount in emerging economies. One of the critical interruptions is the targeted attacks on the operating cyber-physical systems to disconnect operations, inspection, or monitoring of the system. Therefore, adopting a cybersecurity system (or platform) that provides holistic protection is vital for protecting the integrity of critical infrastructure networks. As such, this research aspires to provide a decision support system for cybersecurity managers or practitioners (in the Indian power sector) to select the best and appropriate platform for protection against cyber-attacks. A three-phase method is adopted. First, a literature search followed by an expert panel discussion identified alternatives (cybersecurity platforms) and selection criteria. Next, a questionnaire was developed. Thirdly, a hybrid Best-Worst Improved and COmprehensive distance-Based RAnking (BWM-I and COBRA) method was proposed and applied to evaluate the cybersecurity platform alternatives. Four alternatives (Cloud-Based Platforms, Web-Based Platforms, Application-Based Platforms, and AI-Based Platforms), six primary criteria, and fifteen unique sub-criteria were identified. Responses were collected from 80 power utility managers on a pan-India basis, ranking "End-to-End Coverage" criteria and the AI-Based platform as best. This approach identified the best cybersecurity platform that, if adopted, can be extended to other critical infrastructures, with an appropriate adjustment in the selection criteria. The study can be helpful to practitioners in evaluating cybersecurity platforms. Furthermore, it addresses a research gap in its application in a developing country like India.

Weaponized disinformation spread and its impact on multi-commodity critical infrastructure networks

The well-publicized disinformation campaigns surrounding recent elections, pandemic vaccination adoption, as well as supply-chain disruptions and shortages have made historical problems of disinformation more apparent. When disinformation targets transportation infrastructure, supply chains can be disrupted, resulting in commodities shortages such as food and fuel, consequently jeopardizing the health and safety of communities. This research proposes an integrated epidemiological-optimization model that quantifies the impacts of weaponized disinformation on infrastructure networks that transport multiple commodities. The model aims to minimize the overall weighted shortage of commodities caused by different disinformation spread rates. Results show that disinformation weaponized against transportation infrastructure, potentially targeting a particular geographical region or a particular commodity, can have wide-ranging impacts across different commodities. The proposed model is applied to the multi-commodity Swedish railway network carrying 14 different categories of commodities over 1360 supply and demand nodes.

Risk analysis of road networks under the influence of landslides by considering landslide susceptibility and road vulnerability: A case study

Landslides are the most common natural hazards and can cause casualties and damage forests, roads, water conservancy, hydropower, and other infrastructure. Road networks are essential components of critical infrastructure networks. Damage from natural disasters significantly impact the production and life in the region. It is important to evaluate the risk of road networks under landslides. In this paper, we investigate the risk of road networks under the influence of landslides in six counties adjacent to Sichuan Province and Yunnan Province by considering landslide susceptibility and road vulnerability. First, according to the characteristics of the study area, six evaluation indicators were selected for landslide susceptibility and road network vulnerability. Second, the combination weighting method is used to assign the weight of each evaluation index. Finally, the risk analysis results combining landslide susceptibility and road network vulnerability are obtained using the landslide risk formula. The research work presented in this paper can help reduce the property loss caused by landslides and realize the timely identification and protection of high-risk road sections.

Threat Hunting System for Protecting Critical Infrastructures Using a Machine Learning Approach

Cyberattacks are increasing in number and diversity in nature daily, and the tendency for them is to escalate dramatically in the forseeable future, with critical infrastructures (CI) assets and networks not being an exception to this trend. As time goes by, cyberattacks are more complex than before and unknown until they spawn, being very difficult to detect and remediate. To be reactive against those cyberattacks, usually defined as zero-day attacks, cyber-security specialists known as threat hunters must be in organizations’ security departments. All the data generated by the organization’s users must be processed by those threat hunters (which are mainly benign and repetitive and follow predictable patterns) in short periods to detect unusual behaviors. The application of artificial intelligence, specifically machine learning (ML) techniques (for instance NLP, C-RNN-GAN, or GNN), can remarkably impact the real-time analysis of those data and help to discriminate between harmless data and malicious data, but not every technique is helpful in every circumstance; as a consequence, those specialists must know which techniques fit the best at every specific moment. The main goal of the present work is to design a distributed and scalable system for threat hunting based on ML, and with a special focus on critical infrastructure needs and characteristics.

Old wine in new bottles? Understanding infrastructure resilience: Foundations, assessment, and limitations

While the trend towards building resilience in infrastructure systems for disaster risk reduction is accelerating, the application of infrastructure resilience is largely conceptual or an embellishment to established modelling techniques. In response, this paper sets out the theoretical foundations of infrastructure resilience, which broadly applies across critical infrastructure networks. This is followed by reviewing system-based and network-based approaches to resilience assessment with a focus on transport infrastructure, where there is an emerging body of studies. It spotlights critical issues in conflating resilience with other concepts of infrastructure safety management or merely tagging resilience on established methods, indicating a phenomenon of “old wine in new bottles”. Also, oversimplifying disruption scenarios does not provide a sufficient basis for informed resilience planning. This paper reminds readers to look beyond resilience as a mere buzzword and offers guidance on how to do so by recognising the theoretical and methodological cornerstones of infrastructure resilience.

Identification of Resilience Dimensions in Critical Transportation Infrastructure Networks

Identification of Resilience Dimensions in Critical Transportation Infrastructure Networks

Critical infrastructure network modelling for flood risk analyses: Approach and proof of concept in Accra, Ghana

AbstractIn flood risk analysis, it is state‐of‐the‐art to determine the direct consequences of flooding for assets and people. Flooding also disrupts critical infrastructure (CI) networks, which are vital in modern society. Cascading effects in a CI network can exceed the hydrological catchment boundaries. The effects of directly impacted CI cascade to other infrastructures, which are thus indirectly affected by a flood. A robust modelling approach of CI networks is a basis for including these effects in flood risk analysis. One challenge is to balance the simplicity of the modelling approach, the reproduction of a CI network's complexity and the decisions made based on potential model outputs. In this article, a topology‐based modelling approach of CI networks for catchment‐wide flood risk analyses is proposed. The basic model elements are points, connectors and polygons, which are utilised to represent a multisectoral and layered CI network. The newly defined approach is implemented as CI network module to the state‐of‐the‐art flood risk analysis framework ProMaIDes. It analyses the CI's direct and cascading impacts as well as the indirect disruption of CI services triggered by flooding scenarios. It quantifies the consequences by determining the number of disrupted CI users or the disruption time. A proof of concept in Accra, Ghana demonstrates the method's capabilities.

Integrating Critical Infrastructure Networks into Flood Risk Management

Critical infrastructure (CI) networks are essential for the survival and functionality of society and the economy. Disruptions to CI services and the cascading effects of these disruptions are not currently included in flood risk management (FRM). The work presented in this study integrates CI into every step of FRM, including flood risk analysis, risk mitigation and risk communication. A CI network modelling technique enables the flood consequences for CI to be quantified as part of the flood risk analysis. The CI consequences derived from this analysis include spatial overviews and the temporal succession of CI disruptions. The number of affected CI end-users and the duration of the disruption are arranged in a risk matrix and in a decision-making matrix. Thus, the total flood risk is extended with CI consequences. By integrating CI and CI network characteristics into the flood risk assessment and the mitigation steps, a wider range of measures for action can be considered. Additionally, the continuous participation of CI operators is introduced as beneficial for every step of the FRM. A case study in Accra, Ghana proves the benefits of CI integration for all FRM steps. During participatory CI stakeholder engagements for this study six CI sectors were identified for the assembly of the CI network. The backbone of the analysis is a multisectoral, layered CI network model with 433 point elements, 1216 connector elements and 486 polygon elements.

Historical Development of Cybersecurity Studies: A Literature Review and Its Place in Security Studies

This study discusses the formation and development of cybersecurity studies since the creation of the Internet. Although the origin of cybersecurity studies dates back to the 1970s, hacking, malicious software, computer intrusions, and espionage attacks that took place in the 1980s led cybersecurity studies to form in the area of computer science. By the 1990s, the Internet began to be used widely, and an increase in the level of attacks in cyberspace began to occur. This period was a major reason for the growth in writing on software and network security. Network security has become a key priority for governments and many industries. Cybersecurity studies have become a priority area in security studies with the increasing complexity of cyber threats towards 2000s. States and some supranational organizations have started to create cybersecurity strategies. The security of critical infrastructure and computer networks has begun to emerge as a high-priority area. It has been observed that the transition from classical security policies to modern security policies, which should be established in the information age, has begun. Cybersecurity studies were taken more seriously after the 2007 Estonian attacks, especially in the 2010s. In this period, the intensity of attacks on critical infrastructures and the occurrence of some physical attacks caused cybersecurity to deepen and become an issue on an international scale. Cybersecurity studies continue to be shaped by being influenced by many different disciplines, regardless of any discipline, with the important discussions and cyber incidents that have taken place in recent years. Therefore, the studies were handled from a multidisciplinary perspective.

Developing a Holistic Resilience Framework for Critical Infrastructure Networks of Buildings and Communities in Saudi Arabia

Cities around the world increasingly recognize the need to build on their resilience to deal with converging forces such as disasters, environmental degradation, urban sprawl, and climate change. Given the significance of critical infrastructure networks (CINs) for maintaining the quality of life in buildings and communities, improving their resilience is of high importance to governors, planners, and policymakers. Therefore, this study is proposed to spatially analyze the resilience of CINs in Saudi Arabia and to develop a holistic resilience framework for buildings and communities. The research method of this study involves a case study of Najran, including a collection of CIN data, history of natural disasters, and future potential hazards. The data were compiled to list the basic parameters required for the development of resilience criteria. Overall results show that CINs in the city of Najran were performing below average compared with the measurement criteria. The study highlights the need to make improvements in terms of the robustness, redundancy, and flexibility of the CINs in the city. Moreover, this paper proposes a holistic framework of key aspects of resilient criteria that need to be taken into account by the city governor, policymakers, and developer bodies for better management of available infrastructure and its development in future years.

Methodology for Resilience Assessment of Oil Pipeline Network System Exposed to Earthquake

The oil pipeline network system (OPNS) is an essential part of the critical infrastructure networks (CINs), and is vulnerable to earthquakes. Assessing and enhancing the resilience of the OPNS can improve its capability to cope with earthquakes or to recover the system’s performance quickly after the disturbance. This study defines the concept of OPNS resilience in the resistive ability, the adaptive ability, and the recovery ability. Then, the quantitative resilience assessment model is established considering the earthquake intensities, the role of safety barriers, the time-variant reliability, and the importance coefficient of each subsystem via a Monte Carlo simulation. Combining the model with GIS technology, a new methodology to evaluate OPNS resilience is proposed, and the resilience partition technology platform is developed, which can visualize the results of the resilience assessment. Finally, a case study is implemented to demonstrate the developed methodology, and a discussion is provided to identify the sensitive variables. The proposed resilience methodology can provide a framework for the probabilistic resilience assessment of OPNS, and could be expanded to other lifeline network systems.

Analyzing the tradeoff between vulnerability and recoverability investments for interdependent infrastructure networks

Critical cyber-physical infrastructure networks such as electric power and water networks form the backbone of a functional society as these networks provide the main resources for economic productivity, healthy communities, and general day-to-day operations. However, (i) these infrastructure networks have become more interdependent on each other in terms of functionality, and (ii) community networks become more dependent on these critical infrastructures in terms of existence. Thus, studying the entirety of their resilience process—both vulnerability and recoverability against a disruption—is of importance, particularly analyzing how investing (i) before to strengthen components versus (ii) afterward to restore disrupted components can affect the resilience of infrastructure networks in terms of system performance and time. This paper takes a first step toward analyzing the tradeoff between reducing vulnerability and enhancing recoverability in interdependent infrastructure networks with multiple time and budget allocations, accounting for not only the performance of the physical networks but also the potentially socially vulnerable communities that rely upon them. The proposed model (i) maximizes a measure of the resilience of the interdependent infrastructure networks, while it (ii) minimizes the total cost associated with the pre- and post-event resource allocation. The model is illustrated with networks in Shelby County, TN, USA.