Survivability Evaluation Method for Cascading Failure of Electric Cyber Physical System Considering Load Optimal Allocation

Qu, Zhaoyang; Dong, Yunchang; Qu, Nan; Wang, Lei; Li, Yahui; Zhang, Yu; Mugemanyi, Sylvere

doi:10.1155/2019/2817586

Cited by 11 publications

(14 citation statements)

References 37 publications

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“…where b is a mathematical expectation of the data packet transmission time; = ∑ -mathematical expectation of the intensity of restoring the functioning of network elements after the successful realization of the cyberattack; с = ∑ -mathematical expectation of the intensity of the information impact against network elements, each of which is accessible for the cybercriminal intelligence system, and realization of cyberattack with probability Pi [3,24]; = ̅ -coefficient that has the meaning of the probability of the successful operation of the network equipment when network traffic is absent until the time of the successful realization of the cyberattack, assuming that during the restoration of the function, the network element is not used for its intended purpose [24].…”

Section: The First Stagementioning

confidence: 99%

“…By carrying out cyberattacks on automated systems and technological DTNs, malefactors disrupt the normal functioning of the power complex elements [3]. Therefore, the automation systems and technological DTNs that are being implemented in power complexes must provide operational management of energy resources, as well as meet the high cybersecurity requirements for both automation system elements and technological DTNs.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Functioning Quality of Data Transmission Networks in the Context of Cyberattacks

et al. 2021

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Cyberattacks against the elements of technological data transmission networks represent a rather significant threat of disrupting the management of regional electric power complexes. Therefore, evaluating the functioning quality of data transmission networks in the context of cyberattacks is an important task that helps to make the right decisions on the telecommunication support of electric power systems. The known models and methods for solving this problem have limited application areas determined by the admissible packet distribution laws. The paper proposes a new method for evaluating the quality of the functioning of data transmission networks, based on modeling the process of functioning of data transmission networks in the form of a stochastic network. The proposed method removes restrictions on the form of the initial distributions and makes the assumptions about the exponential distribution of the expected time and packet servicing in modern technological data transmission networks unnecessary. The method gives the possibility to evaluate the quality of the network functioning in the context of cyberattacks for stationary Poisson transmission and self-similar traffic, represented by Pareto and Weibul flows models. The obtained evaluation results are in good agreement with the data represented in previously published papers.

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Section: The First Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating the Functioning Quality of Data Transmission Networks in the Context of Cyberattacks

et al. 2021

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“…The experimental environment was constructed by designing the physical layer, cyber layer, and the coupling relationship as the research object [6].…”

Section: A Experimental Environment Designmentioning

confidence: 99%

Power Cyber-Physical System Risk Area Prediction Using Dependent Markov Chain and Improved Grey Wolf Optimization

Xie

Liu

et al. 2020

IEEE Access

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Existing power cyber-physical system (CPS) risk prediction results are inaccurate as they fail to reflect the actual physical characteristics of the components and the specific operational status. A new method based on dependent Markov chain for power CPS risk area prediction is proposed in this paper. The load and constraints of the non-uniform power CPS coupling network are first characterized, and can be utilized as a node state judgment standard. Considering the component node isomerism and interdependence between the coupled networks, a power CPS risk regional prediction model based on dependent Markov chain is then constructed. A cross-adaptive gray wolf optimization algorithm improved by adaptive position adjustment strategy and cross-optimal solution strategy is subsequently developed to optimize the prediction model. Simulation results using the IEEE 39-BA 110 test system verify the effectiveness and superiority of the proposed method.INDEX TERMS Cyber-physical system, Markov chain, risk region prediction, cross-adaptive grey wolf optimization.

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“…With the continuous development of information technology, the interaction between information flow and energy flow in power systems is becoming more and more frequent (Yu and Xue, 2016;Xu et al, 2018;Qu et al, 2019). By integrating computing systems, communication networks and physical environments, traditional power systems have evolved into cyber-physical power systems (CPPS) (Yang et al, 2019;He et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

False Data Injection Attack Detection in Power Systems Based on Cyber-Physical Attack Genes

Dong

et al. 2021

Front. Energy Res.

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In the process of the detection of a false data injection attack (FDIA) in power systems, there are problems of complex data features and low detection accuracy. From the perspective of the correlation and redundancy of the essential characteristics of the attack data, a detection method of the FDIA in smart grids based on cyber-physical genes is proposed. Firstly, the principle and characteristics of the FDIA are analyzed, and the concept of the cyber-physical FDIA gene is defined. Considering the non-functional dependency and nonlinear correlation of cyber-physical data in power systems, the optimal attack gene feature set of the maximum mutual information coefficient is selected. Secondly, an unsupervised pre-training encoder is set to extract the cyber-physical attack gene. Combined with the supervised fine-tuning classifier to train and update the network parameters, the FDIA detection model with stacked autoencoder network is constructed. Finally, a self-adaptive cuckoo search algorithm is designed to optimize the model parameters, and a novel attack detection method is proposed. The analysis of case studies shows that the proposed method can effectively improve the detection accuracy and effect of the FDIA on cyber-physical power systems.

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Survivability Evaluation Method for Cascading Failure of Electric Cyber Physical System Considering Load Optimal Allocation

Cited by 11 publications

References 37 publications

Evaluating the Functioning Quality of Data Transmission Networks in the Context of Cyberattacks

Evaluating the Functioning Quality of Data Transmission Networks in the Context of Cyberattacks

Power Cyber-Physical System Risk Area Prediction Using Dependent Markov Chain and Improved Grey Wolf Optimization

False Data Injection Attack Detection in Power Systems Based on Cyber-Physical Attack Genes

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