Critical Infrastructure Networks Research Articles

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.

The Research On Statistical Identification of Critical Infrastructure Chain Failure Paths

Using literature studies, case studies, empirical induction and statistical identification, we proposed an analytical framework based on empirical induction to identify and analyze chain failure problems among interdependent critical infrastructures. A case database of critical infrastructure chain failures was established, and as the research emphasis the identification method of critical infrastructure chain failure paths was constructed and designed. The chain failure paths of the critical infrastructure and its sub-networks in China and other countries and regions were discussed. Logical relationships of critical infrastructure chain failures were extracted from the established database covering 229 critical infrastructure chain failure cases. Statistics, identification, and analysis of data were conducted to form a quantitative assessment and understanding of the vulnerability of interdependent critical infrastructure networks. Finally, identification methods and probabilistic statistical methods for chain failure paths of critical infrastructure were designed and applied to the case base of this paper. Using this approach, fatal and severe failure paths of critical infrastructure can be identified and the efficiency of the relevant authorities' inspection work will be improved under cost constraints.

Full-time scale resilience enhancement strategies for an integrated electricity-gas-transportation coupled system

The frequent occurrence of extreme disaster events with low probability and high risk poses a serious threat to the safe and stable operation of power grids. With the development of energy internet technology, the deep integration of power distribution, natural gas and transportation networks has opened a promising way for resilient distribution networks. This paper proposes a full-time scale resilience enhancement framework for an integrated electricity-gas-transportation coupled system oriented to energy internet. In this framework, a minimax regret robust optimization model considering the uncertainty of extreme disaster attacks is developed to make the line hardening strategy in the pre-disaster stage, which is solved by the column-and-constraint generation algorithm. Furthermore, the synthetic restoration model considering the co-optimization of dynamic dispatch of gas-fired units, network reconfiguration and damaged component repair is established, in which the coupling influence of the gas and transportation networks on the restoration strategy during the disaster and post disaster stages is taken into account. And the sequential cone programming method and generalized Benders decomposition algorithm is implemented to solve the above model. Finally, the proposed model and solution method are validated via a modified 33-bus power distribution network coupled with a 12-node transportation network and a 7-node gas network. The simulation results demonstrate that the proposed minmax regret robust optimization model can achieve a pre-disaster defensive strategy with less conservativeness in contrast to the conventional robust optimization model. Specifically, the cost under the worst-case attacks by the former has reduced by 11.52–22.29% compared to the latter. Moreover, the influence of the interdependent critical infrastructure networks on the synthetic restoration strategy in the disaster and post-disaster stages has been investigated. In particular, the power output by gas-fired units has reduced by 2699 kW when considering the operation constraints of the gas network. Additionally, due to the time-varying characteristics of traffic flow and commuting time in the transportation network, the repair time for the damaged components is shortened by 1.09 h while the preparation time of crews changes from 2.5 to 1.5 h.

Attack-Defense game analysis of critical infrastructure network based on Cournot model with fixed operating nodes

In recent years, some game models were proposed to protect critical infrastructure networks. But they mainly focused on the protection of key nodes, and there are rarely models to consider the fixed-point use of resources. Hence, in this paper, we propose a non-zero-sum simultaneous game model based on the Cournot model. Meanwhile, we presented a novel method of critical node centrality identification based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Simulating the game analysis on scale-free networks, small-world networks and random networks, it is found that the fixed operating nodes and the network topology are key factors in payoffs considering the constraints of resources. Besides, robustness analysis of networks on various sensitivity parameters is given and some effective optimal strategies are acquired to provide decision support for policy-makers.

КИБЕРБЕЗОПАСНОСТЬ СЕТЕВОГО ПЕРИМЕТРА ОБЪЕКТА КРИТИЧЕСКОЙ ИНФОРМАЦИОННОЙ ИНФРАСТРУКТУРЫ

The purpose of this paper is an analytical pre-project study of possible technological aspects of countering external computer attacks on critical network infrastructure. This will make it possible to specify the tasks for further resolving this problem in the aspect of developing the necessary software and hardware. The practical implementation of such tasks is an urgent and rather unconventional problem due to various factors of change in the classical concept of the network perimeter as a physical boundary of the information infrastructure, which becomes virtual and, therefore, requires the use of new approaches to the development of technical solutions. Based on statistical data on the number and quality of computer incidents, the study provides a justification for the relevance of the above problem, and gives an overview of widely used technical means for protecting the classic network perimeter, such as firewalls and systems for detecting attacks and intrusions. A comparative analysis of modern technological trends in their development, referred to in publications as «Threat Detection and Response», «Extended Detection and Response», is carried out. However, despite the powerful software and hardware functionality of these solutions, their common drawback is indicated as the lack of adequate counteraction to computer attacks with a remote mode of the user work. In this regard, the latest concept of virtual network perimeter protection, referred to by the authors as «Cybersecurity Mesh» («cybersecurity network»), is detailed. It is this methodology that seems to be the most promising for the development of appropriate technological solutions to ensure the cybersecurity of the perimeter of the critical information infrastructure. The paper might be useful to specialists working on the security of critical information infrastructure facilities, as well as to employees of educational classes in the implementation of appropriate training, retraining and advanced training programs for such specialists.

Performance-based maintenance analysis and resource allocation in irrigation networks

The irrigation network is a critical infrastructure network consisting of reservoirs, canals, and croplands. However, the operation of irrigation networks is often disrupted by natural disasters like droughts, which can lead to serious losses. Timely and effective maintenance can minimize the yield losses. Many maintenance strategies have been proposed to improve the efficiency of maintenance resource utilization and reduce costs. But in the existing literature, the maintenance of irrigation networks under the influence of droughts is still under-explored. To bridge this gap, this paper proposes a maintenance metric for improving the performance of an irrigation network. A novel optimum maintenance efficiency model is then developed to get the optimal resource allocation. A case study, which investigates an irrigation network consisting of 27 nodes, is used to verify the practicality and effectiveness of the proposed method.

Anomaly Detection in Industrial IoT Using Distributional Reinforcement Learning and Generative Adversarial Networks.

Anomaly detection is one of the biggest issues of security in the Industrial Internet of Things (IIoT) due to the increase in cyber attack dangers for distributed devices and critical infrastructure networks. To face these challenges, the Intrusion Detection System (IDS) is suggested as a robust mechanism to protect and monitor malicious activities in IIoT networks. In this work, we suggest a new mechanism to improve the efficiency and robustness of the IDS system using Distributional Reinforcement Learning (DRL) and the Generative Adversarial Network (GAN). We aim to develop realistic and equilibrated distribution for a given feature set using artificial data in order to overcome the issue of data imbalance. We show how the GAN can efficiently assist the distributional RL-based-IDS in enhancing the detection of minority attacks. To assess the taxonomy of our approach, we verified the effectiveness of our algorithm by using the Distributed Smart Space Orchestration System (DS2OS) dataset. The performance of the normal DRL and DRL-GAN models in binary and multiclass classifications was evaluated based on anomaly detection datasets. The proposed models outperformed the normal DRL in the standard metrics of accuracy, precision, recall, and F1 score. We demonstrated that the GAN introduced in the training process of DRL with the aim of improving the detection of a specific class of data achieves the best results.

Adaptive algorithm for dependent infrastructure network restoration in an imperfect information sharing environment.

Critical infrastructure networks are vital for a functioning society and their failure can have widespread consequences. Decision-making for critical infrastructure resilience can suffer based on several characteristics exhibited by these networks, including (i) that there exist interdependencies with other networks, (ii) that several decision-makers represent potentially competing interests among the interdependent networks, and (iii) that information about other decision-makers’ actions are uncertain and potentially unknown. To address these concerns, we propose an adaptive algorithm using machine learning to integrate predictions about other decision-makers’ behavior into an interdependent network restoration planning problem considering an imperfect information sharing environment. We examined our algorithm against the optimal solution for various types, sizes, and dependencies of networks, resulting in insignificant differences. To assess the proposed algorithm’s efficiency, we compared its results with a proposed heuristic method that prioritizes, and schedules components restoration based on centrality-based importance measures. The proposed algorithm provides a solution sufficiently close to the optimal solution showing the algorithm performs well in situations where the information sharing environment is incomplete.

Generating Synthetic Systems of Interdependent Critical Infrastructure Networks

The lack of data on critical infrastructure systems has hindered the research progress in modeling and optimizing the system performance. This work develops a method for generating Synthetic Interdependent Critical Infrastructure Networks (SICIN) using simulation and non-linear optimization techniques. SICIN consists of three components: (i) determining the location of facilities in individual networks via a modified simulated annealing algorithm, (ii) generating interdependent links based on a novel pseudo-tripartite graph algorithm, and (iii) simulating network flow using nonlinear optimization considering the operations of individual networks and their interdependencies. Two existing systems of interdependent infrastructure networks are used to validate the proposed method. The results demonstrate that SICIN outperforms state-of-the-art simulation methods according to multiple topological and flow measures of similarity between the simulated and real networks.

Dynamics of node cascading model with source-sink edges

The load is a very important physical quantity for cascading failures in critical infrastructure networks. However, in previous studies, regardless of the node cascading failure model or the edge cascading failure model, all load in the network is born on the nodes ignoring the possibility of load pouring into the network from edges. For example, almost all vehicles from communities, units, parking lots, etc. are flooding into the transportation network from roads. To this end, considering that the starting point and the destination of the transferred load are located on the “edge”, we give a new method to calculate the initial load on the node and construct a node cascading model with source-sink edges. According to the information about the degree of two nodes connecting the edge, we define the edge weight and give the preferential mechanism of load destination selection. In addition, different from previous models, the load transmitted between two edges will be evenly distributed on all nodes of the shortest paths. By removing the node with the highest load in some artificial networks and two real networks, we study the cascading dynamics. We find the interesting phenomenon of the capacity paradox that the higher capacities of all nodes do not mean that the network has the higher robustness. By two metrics to quantify the network robustness, we further evaluate the robust mode of the network under different parameters, and study the optimal cost to suppress cascading failures in some networks.

Comparative Numerical Studies on the Structural Behavior of Buried Pipes Subjected to Extreme Environmental Actions.

Globally, there are several critical infrastructure networks (water and gas networks) whose disruption or destruction would significantly affect the maintenance of vital societal functions, such as the health, safety, security, and social or economic well-being of people. They would also have significant local, regional, and national impacts as a result of the inability to maintain those functions, and would have similar cross-border effects. The main objective of this article is to investigate by comparative numerical studies the structural response of three types of buried pipes made of different materials, primarily steel, concrete, and high-density polyethylene, resulting from the impact of the environment through exceptional external actions, such as explosions at the surface of the land in the vicinity of the laying areas. The dynamic transient analysis of the equation of motion with the application of the explicit integration procedure was performed with the ANSYS numerical simulation program. This study allows designers to solve complex problems related to the quality of the laying ground of water networks to canals. The knowledge accumulated gives us the possibility to correctly specify the optimal economic and technical value of the ratio between the laying depth of pipes and their diameter, the importance of the radius ratio of the pipe and the thickness of its wall, and, importantly, the improvement of the quality of the foundation ground. Following the results obtained, it is estimated that the optimal economic and technical value of the ratio between the laying depth of the pipes (H) and their diameter (D) is 3, regardless of the material from which the pipe is made.