Hybrid systems modeling for critical infrastructures interdependency analysis

Heracleous, Constantinos; Kolios, Panayiotis; Panayiotou, Christos G.; Ellinas, Georgios

doi:10.1016/j.ress.2017.03.028

Cited by 61 publications

(22 citation statements)

References 37 publications

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“…Additionally, Heracleous et al [56] proposed a dependency modeling method that supports the investigation of the cascading effect, performs vulnerability analysis, and plans maintenance strategies. The authors demonstrated how an open hybrid automata allows modeling individual subsystems and composing them together to create more complex and detailed systems with the aim to capture different types of dependencies.…”

Section: Interdependency Modelsmentioning

confidence: 99%

“…State machine [53,54,56] Stochastic Activity Networks (SAN) [52][53][54] Stochastic Well-formed Nets (SWN) [52] Competing Risks Theory [53] Monte Carlo Simulation [53] Various data mining methods [59,65,69,71,73,75,76] [82] exploration. While the reviewed papers have not been dedicated to producing a visual aid, we compare visual dimensions to understand the role of visualization for analytical analysis.…”

Section: Attack Detectionmentioning

confidence: 99%

“…Sicari at al. [62] Falco et al [69] He et al [77] Dovom et al [79] Kumar et al [80] Li et al [85] Wang et al [71] Azmoodeh et al [86] Nazeeruddin [66] Meidan et al [81] Azmoodeh et al [78] Both Li et al [58] Mohsin et al [68] Wang et al [63] Angelini et al [70] König et al [49] Baracaldo et al [87] Laishram and Phoha [88] Stergiopoulos et al [50] Both Shivraj et al [67] Bou-Harb et al [73] Kotzanikolaou et al [60] R et al [82] Raza et al [83] Shreenivas et al [84] Stergiopoulos et al [51] Oza et al [76] Kelarestaghi et al [59] Bloomfield et al [53] Beccuti et al [52] Laugé et al [48] Johansen et al [55] Heracleous et al [56] Ferdowsi et al [57] Fig . 9 The distribution of visualization types.…”

Section: Performance Of the Modelmentioning

confidence: 99%

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A survey of methods supporting cyber situational awareness in the context of smart cities

et al. 2020

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A modern urban infrastructure no longer operates in isolation, but instead, leverages the latest technologies to collect, process, and distribute aggregated knowledge in order to improve the quality of the provided services and promote the efficiency of resource consumption. This technological development, however, manifests in the form of new vulnerabilities and a plethora of attack vectors. In the same context, the ambiguity of ever-evolving cyber threats and their debilitating consequences introduce new barriers for decision-makers. Therefore, cyber situational awareness of smart cities emerges as a mission-critical task that requires support methods for effective and timely decision-making. In this article, we investigate the threat landscape of smart cities, survey and reveal the progress in data-driven methods for situational awareness and evaluate their effectiveness when addressing various cyber threats. We draw several potential research directions that aim at advancing cyber situational awareness in the context of smart cities.

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Section: Interdependency Modelsmentioning

confidence: 99%

Section: Attack Detectionmentioning

confidence: 99%

Section: Performance Of the Modelmentioning

confidence: 99%

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A survey of methods supporting cyber situational awareness in the context of smart cities

et al. 2020

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“…Because the removal of a part of critical nodes or edges is prone to greatly impair the ability to normally operate in real-world systems, especially for the interdependent infrastructure network, the approaches regarding the identification of key nodes and edges for the interdependent power-communication network were proposed [19], [20], respectively. In order to understand the response of the interdependent system composed of the power grid, the corresponding cascading failure models were built for interdependent power-transportation networks [21]- [23], interdependent power-telecom-water networks [24], and interdependent energy-power networks [25], [26], respectively. Additionally, Marzuoli et al gave a case report with respect to the Asiana crash in San Francisco International Airport, finding that cancelations and delays in the aerospace lead to the occurrence of a traffic jam in the road near the airport, and the increased transit passenger demand [27].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Resilience Under Repair Strategy in Interdependent Mechatronic System

et al. 2021

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Existing works on the interdependent system have come to fruition based on the percolation theory and revealed that it possesses the great vulnerability due to the essential interdependency. However, how to effectively recover the performance of the interdependent system after cascading failures is still under research. In this article, we define an interdependent mechatronic system as an interdependent machineelectricity-communication network. By considering the behavior of the real-world system, we put forward an extended cascading failure model in which the non-giant component is also functional when its size proportion is not smaller than the proportion threshold δ and it has the interdependent links from the other two subnetworks. Then, according to the measures of a node (i.e., single measures), the interdependent measures are proposed and the repair strategies are obtained by these measures to determine the order of repaired nodes. In order to accurately reflect the resilience in the interdependent mechatronic system, we adopt three metrics to quantify it, i.e., the change of the robustness, the recovery ability, and the critical number of repaired nodes. Finally, we study the relationship between δ and the robustness, and apply different repair strategies to the analysis of the resilience in a real mechatronic system. The experiments show that the non-giant component plays a key role in the robustness and the resilience is affected by δ when a few nodes fail to work. In addition, we obtain the optimal repair strategy from different aspects of the resilience. A striking finding is that in most instances, the repair strategies concerning the interdependent measures lead to the higher resilience compared with the ones concerning the single measures. Our work may provide insights to make a plan for repairing equipment so as to enhance the resilience of the interdependent mechatronic system.

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“…System dynamics originates from system theory, so it can apply to any system. However, the utilization of this method is overwhelmingly dependent on the knowledge of a subject-matter expert and it is a semi-quantitative method [ 28 , 29 ]. As for an agent-based model and complex network theory, although both are quite useful, a UAV swarming system can naturally be mapped into complex networks to describe its characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Complex Network Theory-Based Modeling Framework for Unmanned Aerial Vehicle Swarms

Wang

Zhang

et al. 2018

Sensors

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Unmanned aerial vehicle (UAV) swarms is an emerging technology that will significantly expand the application areas and open up new possibilities for UAVs, while also presenting new requirements for the robustness and reliability of the UAV swarming system. However, its complex and dynamic characteristics make it extremely challenging and uncertain to model such a system. In this study, to reach a full understanding of the swarming system, a modeling framework based on complex network theory is presented. First, the scope of work is identified from the point of view of control algorithms considering the dynamics and research novelty of the development of UAV swarming control strategy and three control structures consisting of three interdependent network layers are proposed. Second, three algorithms that systematically build the modeling framework considering all characteristics of the system are also developed. Finally, some network measurements are introduced by adjusting the fundamental ones into the UAV swarming system. The proposed framework is applied to a case study to illustrate the visualization models and estimate the statistical characteristics of the proposed networks with static and dynamic topology analysis. Furthermore, a simple demonstration of the robustness evaluation of the network is also presented. The networks obtained from this framework can be used to further analyze the robustness or reliability of a UAV swarming system in a high-confrontation battlefield environment the effect of cascading failure in ad-hoc network on system.

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Hybrid systems modeling for critical infrastructures interdependency analysis

Cited by 61 publications

References 37 publications

A survey of methods supporting cyber situational awareness in the context of smart cities

A survey of methods supporting cyber situational awareness in the context of smart cities

Analysis of Resilience Under Repair Strategy in Interdependent Mechatronic System

A Complex Network Theory-Based Modeling Framework for Unmanned Aerial Vehicle Swarms

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