Critical Infrastructure Networks Research Articles

Geoinformation system approach to the analysis of the impact of armed conflicts on the ecological condition of the environment

The article considers the experience of solving problems in assessing the impact of armed conflict on the environment. An analysis of international approaches implemented in the framework of relevant UN resolutions to prevent critical changes in environmental parameters caused by direct and indirect effects of armed conflict. The analysis showed that in the conditions of the armed conflict on the territory of Donbass unbalanced military natural-technogenic geosystem MNTGS was formed, which is characterized by destruction and pollution of soils, deterioration of biodiversity, destruction of potentially dangerous objects due to fires and active hostilities. In addition, the decommissioning and flooding of a large number of coal mines increases the environmental and man-made impact of MNTGS on the natural environment and safety of life. Taking into account the above, the article presents the main results of the development of a generalized model for assessing the impact of factors of armed conflict on all components of the environment. Suggested to intensify environmental monitoring of the areas of the armed conflict and increase its information content through the use of GIS, remote sensing and mathematical modeling technologies; organize measures to increase the resilience and accelerate the restoration of critical infrastructure networks in Donbass; to begin research of the newest ecological-technogenic and ecological-resource factors of military-technogenic threats in Europe's largest man-made coal region.

Model and solution method for mean-risk cost-based post-disruption restoration of interdependent critical infrastructure networks

Critical infrastructure networks (CINs), such as power grids, water distribution systems, and telecommunication networks, are essential for the functioning of society and the economy. As these infrastructure networks are not isolated from each other, their functions are not independent and may be vulnerable to disruptive events (e.g., component failures, terrorist attacks, natural disasters). For decision makers, how to restore the functions of CINs while accounting for interdependencies and various uncertainties becomes a challenging task. In this work, we study the post-disruption restoration problem for a system of interdependent CINs under uncertainty. We propose a two-stage mean-risk stochastic restoration model using mixed-integer linear programming (MILP) with the goal of minimizing the total cost associated with unsatisfied demands, repair tasks, and flow of interdependent infrastructure networks. The restoration model considers the availability of limited time and resources and provides a prioritized list of components to be restored along with assigning and scheduling them to the available network-specific work crews. Additionally, the model features flexible restoration strategies including multicrew assignment for a single component and a multimodal repair setting along with the consideration of full and partial functioning and dependencies between the multi-network components. The proposed model is illustrated using the power and water networks in Shelby County, Tennessee, United States, under two hypothetical earthquake scenarios.

Utilizing Switch Port Link State to Detect Rogue Switches

There are many methods to detect rogue wireless access points, but the same case can not be said for rouge switches on a LAN network. Detecting these rogue switches is key to security of any organization. The introduction of a rogue unmanaged network switch has the potential to cripple a network. These types of switches pose a big risk because they are usually plug and play types of devices and can prove difficult to track. A switch becomes rouge when it is connected to a network without proper authorization. Rogue switches are a huge threat to the security and reliability of any network. An attacker could use a rogue switch to launch an attack or spy on network traffic information. Many organizations these days implement a “bring your own device” policy that can prove to be a daunting task to monitor for any network administrator. It is important that these rogue network switches are not introduced to a network, whether by accident or in a malicious attempt. The vulnerability that is introduced could comprise the confidentiality of network messages, degrade network performance, or even allow hackers or authorized users access to critical network infrastructure and data. In this paper we present a method that can help detect these rogue switches by monitoring the ethernet frames across the network and looking at the link state of the network switch ports. We will be using Wireshark, a Windows Computer, and a local switch setup to test methods for detecting a rogue switch. In our scenario we were able to provide some evidence of amethod that could be used in conjuncture with other rules and policies to detect rogue switches connected to a network. We were able to determine based on the port link state that there was another device, most likely a rogue switch between the good switch and the computer.

Determination of the Most Interconnected Sections of Main Gas Pipelines Using the Maximum Clique Method

This article is devoted to the definition of the most important combinations of objects in critical network infrastructures. This study was carried out using the example of the Russian gas transmission network. Since natural gas is widely used in the energy sector, the gas transmission network can be exposed to terrorist threats, and the actions of intruders can be directed at both gas fields and gas pipelines. A defender–attacker model was proposed to simulate attacks. In this model, the defender solves the maximum flow problem to satisfy the needs of gas consumers. By excluding gas pipelines, the attacker tries to minimize the maximum flow in the gas transmission network. Russian and European gas transmission networks are territorially very extensive and have a significant number of mutual intersections and redundant pipelines. Therefore, one of the approaches to inflicting maximum damage on the system is modeled as an attack on a clique. A clique in this study is several interconnected objects. The article presents the list of the most interconnected sections of main gas pipelines, the failure of which can cause the greatest damage to the system in the form of a gas shortage among consumers. Conclusions were drawn about the applicability of the maximum clique method for identifying the most important objects in network critical infrastructures.

Modelling of multi-sectoral critical infrastructure interdependencies for vulnerability analysis

Critical infrastructure such as the transportation, power generation, water supply, telecommunications, security and health services/systems, etc. are essential for providing a reliable flow of goods and services, crucial to the functioning of the economy and society. These infrastructures are closely linked and dependent on one another, and these interdependencies need to be modelled in order to analyse the disruptions and vulnerabilities of critical infrastructure networks as a whole. With increased, investment and complexity in the coupling of gas and electricity network, limitations and vulnerabilities of the coupled networks are becoming increasingly relevant to the operational planning of the critical infrastructures. Current modelling of a coupled gas and electricity network will be used in conjunction with nation input-output interdependency model to model physical critical infrastructures and critical infrastructure interdependencies, respectively. This research work will tackle two possible scenarios that might happen in the gas network while evaluating the cascading impact both in the physical model perspective and input-output interdependency model perspective. The results will provide insights on how disruption in the gas network affects the electricity grid and its corresponding economic impact on all economic sectors in a nation.

Graph Neural Network and Koopman Models for Learning Networked Dynamics: A Comparative Study on Power Grid Transients Prediction

Continuous monitoring of the spatio-temporal dynamic behavior of critical infrastructure networks, such as the power systems, is a challenging but important task. In particular, accurate and timely prediction of the (electro-mechanical) transient dynamic trajectories of the power grid is necessary for early detection of any instability and prevention of catastrophic failures. Existing approaches for prediction of dynamic trajectories either rely on the availability of accurate physical models of the system, use computationally expensive time-domain simulations, or are applicable only at local prediction problems (e.g., a single generator). In this paper, we report the application of two broad classes of data-driven learning models &#x2013; along with their algorithmic implementation and performance evaluation &#x2013; in predicting transient trajectories in power networks using only streaming measurements and the network topology as input. One class of models is based on the <i>Koopman operator theory</i> which allows for capturing the nonlinear dynamic behavior via an infinite-dimensional linear operator. The other class of models is based on the <i>graph convolutional neural networks</i> which are adept at capturing the inherent spatio-temporal correlations within the power network. Transient dynamic datasets for training and testing the models are synthesized by simulating a wide variety of load change events in the IEEE 68-bus system, categorized by the load change magnitudes, as well as by the degree of connectivity and the distance to nearest generator nodes. The results confirm that the proposed predictive models can successfully predict the post-disturbance transient evolution of the system with a high level of accuracy.

Computing Effective Mixed Strategies for Protecting Targets in Large-Scale Critical Infrastructure Networks

Most critical infrastructure networks often suffer malicious attacks, which may result in network failures. Therefore, how to design more robust defense measures to minimize the loss is a great challenge. In recent years, defense strategies for enhancing the robustness of the networks are developed based on the game theory. However, the aforementioned method cannot effectively solve the defending problem on large-scale networks with a full strategy space. In this study, we achieve the purpose of protecting the infrastructure networks by allocating limited resources to monitor the targets. Based on the existing two-person zero-sum game model and the Double Oracle framework, we propose the EMSL algorithm which is an approximation algorithm based on a greedy search to compute effective mixed strategies for protecting large-scale networks. The improvement of our approximation algorithm to other algorithms is discussed. Experimental results show that our approximation algorithm can efficiently compute the mixed strategies on actual large-scale networks with a full strategy space, and the mixed defense strategies bring the highest utility to a defender on different networks when dealing with different attacks.

Cascading failures in anisotropic interdependent networks of spatial modular structures

The structure of real-world multilayer infrastructure systems usually exhibits anisotropy due to constraints of the embedding space. For example, geographical features like mountains, rivers and shores influence the architecture of critical infrastructure networks. Moreover, such spatial networks are often non-homogeneous but rather have a modular structure with dense connections within communities and sparse connections between neighboring communities. When the networks of the different layers are interdependent, local failures and attacks may propagate throughout the system. Here we study the robustness of spatial interdependent networks which are both anisotropic and heterogeneous. We also evaluate the effect of localized attacks having different geometrical shapes. We find that anisotropic networks are more robust against localized attacks and that anisotropic attacks, surprisingly, even on isotropic structures, are more effective than isotropic attacks.

Adversarial Hiding Deception Strategy and Network Optimization Method for Heterogeneous Network Defense

Heterogeneous networks are powerful tools for describing different types of entities and relationships and are more relevant models of complex networks. The study of heterogeneous network defense is of great practical significance for protecting useful networks such as military combat networks and critical infrastructure networks. However, a large amount of current research on complex network defense focuses on homogeneous networks under complete information conditions, which often ignore the real conditions such as incomplete information and heterogeneous networks. In this paper, we propose firstly a new adversarial hiding deception strategy for heterogeneous network defense under incomplete information conditions. Secondly, we propose an adversarial hiding deception network optimization method based on a genetic algorithm and design node importance index and a fitness function, which take into account the graph structure information and information about the type of nodes. Finally, we conduct comparison experiments for different defense strategies, and the results show that the proposed strategy and network optimization method are effective at hiding the critical nodes and inducing the attacker to attack the non-important nodes. The generated adversarial hiding deception network has a similar graph structure to the real network.

Communication Challenges for Electric Power Transmission Systems

Uninterrupted electric power supply and transmission is a part of critical infrastructure for any nation’s security, economy and healthcare systems. Essential requirements for operating and managing an industrial control system is the secure and instant data transfer between control centers and generation stations or substations. This paper review the general Energy Management System architecture implemented for the Hellenic Power Transmission System. It gives a possible direction to best suited SCADA system network communication by emphasizing on the security. A brief study of the communication protocols is also presented. This work focuses on the key role of telecommunication provision for critical distributed control infrastructures. Based on the relative analysis presented in this paper directions towards a secure critical infrastructures network communications are provided. Subject Classification Numbers: 68M01, 68M10, 94A05.

Communication Challenges for Electric Power Transmission Systems

Uninterrupted electric power supply and transmission is a part of critical infrastructure for any nation’s security, economy and healthcare systems. Essential requirements for operating and managing an industrial control system is the secure and instant data transfer between control centers and generation stations or substations. This paper review the general Energy Management System architecture implemented for the Hellenic Power Transmission System. It gives a possible direction to best suited SCADA system network communication by emphasizing on the security. A brief study of the communication protocols is also presented. This work focuses on the key role of telecommunication provision for critical distributed control infrastructures. Based on the relative analysis presented in this paper directions towards a secure critical infrastructures network communications are provided. Subject Classification Numbers: 68M01, 68M10, 94A05.

Managing Physical and Economic Risk for Systems with Multidirectional Network Interdependencies.

Critical infrastructure networks, such as transportation and supply chains, are becoming increasingly interdependent. As the operability of network nodes relies on the operability of connected nodes, network disruptions have the potential to spread across entire networks, having catastrophic consequences in the realms of physical network performance and also economic performance. While risk-informed physical network models and economic models have been well-studied in the literature, there is limited study of how physical features of network performance interact with sector-specific economic performance, particularly as these physical networks recover from disruptions of varying durations. In this article, we create a generalizable framework for integrating Functional Dependency Network Analysis (FDNA) and Dynamic Inoperability Input-Output Models (DIIM), to assess the extent to which disruptions to critical infrastructure could degrade its functionality over a period of time. We demonstrate the framework using disruptive scenarios for a critical transportation network in Virginia, USA. We consider scenarios involving: (a) mild case that is relatively more frequent such as recurring traffic conditions; (b) moderate case involving an incident with a multihour delay, and (c) severe case that is relatively less frequent such as evacuation after a major hurricane. The results will be useful for network managers, policymakers, and stakeholders who are seeking to invest in risk mitigation for network functionality and economic activity.

G-CAS: Greedy Algorithm-Based Security Event Correlation System for Critical Infrastructure Network

The attacks on the critical infrastructure network have increased sharply, and the strict management measures of the critical infrastructure network have caused its correlation analysis technology for security events to be relatively backward; this makes the critical infrastructure network’s security situation more severe. Currently, there is no common correlation analysis technology for the critical infrastructure network, and most technologies focus on expanding the dimension of data analysis, but with less attention to the optimization of analysis performance. The analysis performance does not meet the practical environment, and real-time analysis is even more impossible; as a result, the efficiency of security threat detection is greatly declined. To solve this issue, we propose the greedy tree algorithm, a correlation analysis approach based on the greedy algorithm, which optimizes event analysis steps and significantly improves the performance, so the real-time correlation analysis can be realized. We first verify the performance of the algorithm through formalization, and then the G-CAS (Greedy Correlation Analysis System) is implemented based on this algorithm and is applied in a real critical infrastructure network, which outperformed the current mainstream products.

Vulnerability analysis of critical infrastructure network

Critical infrastructure networks such as transportation networks, power grids, internet, etc. play a crucial role in the economic development of a country and also in the well-being of its citizens. These systems are however vulnerable to failures due to natural disasters, component aging, terrorist attacks and so on and the resulting service disruptions may result in debilitating impacts on the whole society. Hence, it is crucial to understand how the failure of the components in such a network affects the performance and integrity of the whole network. In order to investigate this, the paper studies the effect of random as well as intentional removal of components (nodes) on the functioning of critical infrastructure networks. The paper also proposes a flow redistribution mechanism incorporating geographic proximities to distribute the load on the failed or removed nodes. Two vulnerability indicators: network efficiency and accessibility have been used to measure the failure consequences in the network after node removals. The proposed framework for vulnerability analysis has been applied to a real world urban transit rail network, a bus network and a combined rapid transit-bus network to test its validity. The results from the study can help to gain foresight in safety management and can thus help to protect the system before failure/harm occurs.

Fractional Dynamics of Stuxnet Virus Propagation in Industrial Control Systems

The designed fractional order Stuxnet, the virus model, is analyzed to investigate the spread of the virus in the regime of isolated industrial networks environment by bridging the air-gap between the traditional and the critical control network infrastructures. Removable storage devices are commonly used to exploit the vulnerability of individual nodes, as well as the associated networks, by transferring data and viruses in the isolated industrial control system. A mathematical model of an arbitrary order system is constructed and analyzed numerically to depict the control mechanism. A local and global stability analysis of the system is performed on the equilibrium points derived for the value of α = 1. To understand the depth of fractional model behavior, numerical simulations are carried out for the distinct order of the fractional derivative system, and the results show that fractional order models provide rich dynamics by means of fast transient and super-slow evolution of the model’s steady-state behavior, which are seldom perceived in integer-order counterparts.

The structure and behaviour of hierarchical infrastructure networks

Critical services depend on infrastructure networks for their operation and any disruption to these networks can have significant impacts on society, the economy, and quality of life. Such networks can be characterised as graphs which can be used to understand their structural properties, and the effect on their behaviour and robustness to hazards. Using a suite of graphs and critical infrastructure networks, this study aims to show that networks which exhibit a hierarchical structure are more likely to be less robust comparatively to non-hierarchical networks when exposed to failures, including those which supply critical services. This study investigates the properties of a hierarchical structure through identifying a set of key characteristics from an ensemble of graph models which are then used in a comparative analysis against a suite of spatial critical infrastructure networks. A failure model is implemented and applied to understand the implications of hierarchical structures in real world networks for their robustness to perturbations. The study concludes that a set of three graph metrics, cycle basis, maximum betweenness centrality and assortativity coefficient, can be used to identify the extent of a hierarchy in graphs, where a lack of robustness is linked to the hierarchical structure, a feature exhibited in both graph models and infrastructure networks.

Towards Resilient Critical Infrastructures: Understanding the Impact of Coastal Flooding on the Fuel Transportation Network in the San Francisco Bay

Sea level rise (SLR) and storm surge inundation are major concerns along the coast of the San Francisco Bay (the Bay Area), impacting both coastal communities and critical infrastructure networks. The oil industry comprises a complex and critical infrastructure network located in the Bay Area. There is an urgent need to assess consequences and identify risk-based solutions to increase the resilience of this industrial network in the Bay Area to SLR and storm surge. In this study, a comprehensive multi-modal network model representing the fuel supply system was built. A total of 120 coastal flooding scenarios, including four General Circulation Models, two Representative Concentration Pathways, three percentiles of future SLR estimates, and five planning horizons (20 year intervals from 2000 to 2100) were considered. The impact of coastal flooding on fuel transportation networks was studied at two different scales: regional and local. At the regional scale, basic network properties and network efficiency were analyzed across multiple flooding scenarios. At the local scale, cascading effects of individual node disruptions were simulated. Based on this research, smarter and more holistic risk-based adaptation strategies can be established which could lead to a more resilient fuel transportation network system.

Percolation Analysis of Brain Structural Network

The brain network is one specific type of critical infrastructure networks, which supports the cognitive function of biological systems. With the importance of network reliability in system design, evaluation, operation, and maintenance, we use the percolation methods of network reliability on brain networks and study the network resistance to disturbances and relevant failure modes. In this paper, we compare the brain networks of different species, including cat, fly, human, mouse, and macaque. The differences in structural features reflect the requirements for varying levels of functional specialization and integration, which determine the reliability of brain networks. In the percolation process, we apply different forms of disturbances to the brain networks based on metrics that characterize the network structure. Our findings suggest that the brain networks are mostly reliable against random or k-core-based percolation with their structure design, yet becomes vulnerable under betweenness or degree-based percolation. Our results might be useful to identify and distinguish brain connectivity failures that have been shown to be related to brain disorders, as well as the reliability design of other technological networks.

Simple method for identifying interdependencies in service delivery in critical infrastructure networks

Critical infrastructure failures from natural hazard events affect the economic and social well-being of communities. This is particularly true in lower income countries, where infrastructure may be less resistant to natural hazards and disaster recovery is often limited by available resources. The interconnectivity of these systems can strongly affect the services they deliver, and the failure of one infrastructure system can result in cascade failures with wide-reaching consequences. Unfortunately, interconnectivity has been particularly difficult to measure. We present a method for identifying service-oriented interdependencies in interconnected networks. The approach uses well-established methods for network analysis and is demonstrated for healthcare services in the Commonwealth of Dominica, a small island state in the Caribbean. We show that critical links in road networks necessary for healthcare service delivery are important for more than just patient access to a facility, but also on the supply chains that enable the hospitals to function (e.g., water, fuel, medicine). Once identified, the critical links can be overlaid with known hazard vulnerabilities to identify the infrastructure segments of highest priority, based on the risk and consequences of failure. An advantage of the approach presented is that it requires relatively little input data when compared to many network prioritization models and can be run using open-source geospatial data such as OpenStreetMap. The method can be expanded beyond road networks to assess the service-oriented criticality of any infrastructure network.

Planning for resilience of water networks under earthquake hazard

Water networks are vulnerable to earthquakes and failures of network components can result in a lack of availability of services, sometimes leading to relocation of the community. In New Zealand, there are statutory requirements for the water network providers to address the resilience of infrastructure assets. This is done by identifying and managing risks related to natural hazards and planning for appropriate financial provision to manage those risks. In addition to this, the impact from the Canterbury region earthquakes has accelerated the need for understanding the potential risk to critical infrastructure networks to minimise socio-economic impact. As such, there is a need for developing pragmatic approaches to deliver appropriate hazard and risk information to the stakeholders.&#x0D; Within the context of improving resilience for water networks, this study presents a transparent and staged approach to risk assessment by adopting three significant steps: (i) to define an earthquake hazard scenario for which the impact needs to be assessed and managed; (ii) to identify vulnerable parts of the network components; and (iii) to estimate likely outage time of services in the areas of interest. The above process is illustrated through a case study with water supply and wastewater networks of Rotorua Lakes Council by estimating ground motion intensities, damage identification and outage modelling affected by number of crews and preferred repair strategies.&#x0D; This case study sets an example by which other councils and/or water network managers could undertake risk assessment studies underpinned by science models and develop resilience management plans.