Quantitative Model of Attacks on Distribution Automation Systems Based on CVSS and Attack Trees
AbstractThis study focuses on the problem of attack quantification in distribution automation systems (DASs) and proposes a quantitative model of attacks based on the common vulnerability scoring system (CVSS) and attack trees (ATs) to conduct a quantitative and systematic evaluation of attacks on a DAS. In the DAS security architecture, AT nodes are traversed and used to represent the attack path. The CVSS is used to quantify the attack sequence, which is the leaf node in an AT. This paper proposes a method to calculate each attack path probability and find the maximum attack path probability in DASs based on attacker behavior. The AT model is suitable for DAS hierarchical features in architecture. The experimental results show that the proposed model can reduce the influence of subjective factors on attack quantification, improve the probability of predicting attacks on the DASs, generate attack paths, better identify attack characteristics, and determine the attack path and quantification probability. The quantitative results of the model’s evaluation can find the most vulnerable component of a DAS and provide an important reference for developing targeted defensive measures in DASs. View Full-Text
Share & Cite This Article
Li, E.; Kang, C.; Huang, D.; Hu, M.; Chang, F.; He, L.; Li, X. Quantitative Model of Attacks on Distribution Automation Systems Based on CVSS and Attack Trees. Information 2019, 10, 251.
Li E, Kang C, Huang D, Hu M, Chang F, He L, Li X. Quantitative Model of Attacks on Distribution Automation Systems Based on CVSS and Attack Trees. Information. 2019; 10(8):251.Chicago/Turabian Style
Li, Erxia; Kang, Chaoqun; Huang, Deyu; Hu, Modi; Chang, Fangyuan; He, Lianjie; Li, Xiaoyong. 2019. "Quantitative Model of Attacks on Distribution Automation Systems Based on CVSS and Attack Trees." Information 10, no. 8: 251.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.