2020
DOI: 10.1109/access.2020.2973030
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Modeling an Attack-Mitigation Dynamic Game-Theoretic Scheme for Security Vulnerability Analysis in a Cyber-Physical Power System

Abstract: The rapid development of advanced information and communication technology has made modern power systems evolve into more complicated cyber-physical power systems (CPPSs) with mutual coupling characteristics between cyber systems and power systems, and at the same time, the CPPSs have to confront some newly emerged risks owing to cyber system unreliability or cyberattacks. In this paper, regarding the cyber and physical attacks in a CPPS, the operation risks and vulnerabilities of transmission lines are discus… Show more

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Cited by 30 publications
(25 citation statements)
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“…The main focus of [46] is to define the formulation of the attack vector with prevention and detection mechanisms for various components, thus cohesively providing countermeasures against cyber–physical security threats. The vulnerabilities and operational threats of transmission lines are considered in [47] regarding cyber and physical attacks in a cyber–physical power network by constructing specific game‐theoretical models.…”
Section: Review Of State‐of‐the‐artmentioning
confidence: 99%
“…The main focus of [46] is to define the formulation of the attack vector with prevention and detection mechanisms for various components, thus cohesively providing countermeasures against cyber–physical security threats. The vulnerabilities and operational threats of transmission lines are considered in [47] regarding cyber and physical attacks in a cyber–physical power network by constructing specific game‐theoretical models.…”
Section: Review Of State‐of‐the‐artmentioning
confidence: 99%
“…Existing studies have addressed the subjects of vulnerability identification in information systems [4], [5], threat analysis for secure software design [6], topic analysis in software repositories [7], management issues related to vulnerabilities in information systems [8], economic issues in the black market of information security threats [9], security vulnerability analysis in different applications, e.g., power systems [10], [11], fog computing-enabled robust demand response with consideration of collusion attacks in the Internet of Energy [12], cross-lingual multi-keyword ranked search with semantic extensions over encrypted data [13], and posters' behavior in online forums [14]. However, so far, there have been few in-depth studies on vulnerabilities dealing with the automated classification and risk-level prediction of vulnerabilities and the automated generation of solutions for various types of vulnerabilities in software and information systems.…”
Section: Figure 1 Annualized Number Of Vulnerabilities In the China mentioning
confidence: 99%
“…Features are extracted from three dimensions: TD-IDF with bigrams, key topics, and record-level statistics, as discussed in detail in Section IV. We conduct experiments for different topic numbers (10,20,30,40, and 50) in this research. We present the classification and prediction results when the topic number is 30 for the following reasons: (1) The classification and prediction performance are similar for different topic numbers.…”
Section: A Data Preparation and Feature Extractionmentioning
confidence: 99%
“…Malicious attacks can be divided into three types, namely False Data Injection Attack (FDIA) [3], False Command Injection Attack (FCIA) [4] and Distributed Denial of Service (DDoS) attack [5] [6]. The former two refer to unauthorized agents intruding a system and falsifying its data.…”
Section: Introductionmentioning
confidence: 99%