Abstract
This 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.
Highlights
The expansion of the construction scale of distribution automation systems (DASs) and the increasing demand for their application have increased the risk of cyber and physical attacks on these systems
A number of studies have developed measures to quantify system risks or attacks, they insufficiently describe attack behavior or attack paths. These measures are affected by subjective factors, which are unsuitable for attack quantification of distribution automation systems
We propose a modeling method for quantifying attacks on DASs based on common vulnerability scoring system (CVSS) and attack trees (ATs) form the perspective of the attacker’s behavior
Summary
The expansion of the construction scale of distribution automation systems (DASs) and the increasing demand for their application have increased the risk of cyber and physical attacks on these systems. Kateb et al [10] developed an optimal structure tree method for risk assessment in a wide-area power system that can minimize the spread of network attacks. Proposed a method for modeling network attacks with a multilevel-layered attack tree (MLL-AT), presented a description language based on the MLL-AT for attacks, and quantified the leaf nodes. This attack tree (AT) was found to be able to accurately model the attacks, especially multilevel network attacks, and can be used to assess system risks. This method lacks a complete attack process identification method, and its ability to analyze attack paths is insufficient
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