Inferring adversarial behaviour in cyber-physical power systems using a Bayesian attack graph approach

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-02-11 DOI:10.1049/cps2.12047
Abhijeet Sahu, Katherine Davis
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Abstract

Highly connected smart power systems are subject to increasing vulnerabilities and adversarial threats. Defenders need to proactively identify and defend new high-risk access paths of cyber intruders that target grid resilience. However, cyber-physical risk analysis and defense in power systems often requires making assumptions on adversary behaviour, and these assumptions can be wrong. Thus, this work examines the problem of inferring adversary behaviour in power systems to improve risk-based defense and detection. To achieve this, a Bayesian approach for inference of the Cyber-Adversarial Power System (Bayes-CAPS) is proposed that uses Bayesian networks (BNs) to define and solve the inference problem of adversarial movement in the grid infrastructure towards targets of physical impact. Specifically, BNs are used to compute conditional probabilities to queries, such as the probability of observing an event given a set of alerts. Bayes-CAPS builds initial Bayesian attack graphs for realistic power system cyber-physical models. These models are adaptable using collected data from the system under study. Then, Bayes-CAPS computes the posterior probabilities of the occurrence of a security breach event in power systems. Experiments are conducted that evaluate algorithms based on time complexity, accuracy and impact of evidence for different scales and densities of network. The performance is evaluated and compared for five realistic cyber-physical power system models of increasing size and complexities ranging from 8 to 300 substations based on computation and accuracy impacts.

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使用贝叶斯攻击图方法推断网络物理电力系统中的对抗行为
高度互联的智能电力系统面临越来越多的漏洞和对抗性威胁。防御者需要主动识别和防御针对电网弹性的网络入侵者的新的高风险访问路径。然而,电力系统中的网络物理风险分析和防御通常需要对对手的行为做出假设,而这些假设可能是错误的。因此,这项工作研究了推断电力系统中对手行为的问题,以改进基于风险的防御和检测。为了实现这一点,提出了一种用于网络对抗性电力系统推理的贝叶斯方法(贝叶斯CAPS),该方法使用贝叶斯网络(BN)来定义和解决电网基础设施中对抗性运动向物理影响目标的推理问题。具体来说,BN用于计算查询的条件概率,例如在给定一组警报的情况下观察事件的概率。贝叶斯CAPS为现实的电力系统网络物理模型构建初始贝叶斯攻击图。这些模型可使用所研究系统收集的数据进行调整。然后,贝叶斯CAPS计算电力系统安全漏洞事件发生的后验概率。针对不同规模和密度的网络,进行了基于时间复杂性、准确性和证据影响的算法评估实验。基于计算和精度影响,评估并比较了从8到300个变电站规模和复杂性不断增加的五个现实网络物理电力系统模型的性能。
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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
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