Research on Evaluation Method of Network Vulnerability in Power Monitoring System

Qian, Kexiang; Jin, Minghui; Zhang, Daojuan; Xiao, Fei; Zhang, Peng

doi:10.1007/978-981-19-1053-1_11

Cited by 1 publication

(1 citation statement)

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“…Li et al [29] use attack distance and atomic weight to optimize the complexity of the attack graph and improve the ant colony algorithm to solve the minimum key attack set through the pheromone adaptive update principle and local search strategy so as to obtain the key vulnerable nodes, but this method does not consider the threat characteristics of vulnerability nodes themselves. Qian et al [30] optimize the attack graph with maximum hop count and reachability probability and quantify the reachability probability of vulnerable nodes. According to the vulnerability measurement value of nodes and paths, the key vulnerable nodes in the network are found, but this method only considers the attack path with the highest vulnerability measurement value.…”

Section: Related Workmentioning

confidence: 99%

Key Vulnerable Nodes Discovery Based on Bayesian Attack Subgraphs and Improved Fuzzy C-Means Clustering

Xu,

Liu,

Sun

et al. 2024

Mathematics

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Aiming at the problem that the search efficiency of key vulnerable nodes in large-scale networks is not high and the consideration factors are not comprehensive enough, in order to improve the time and space efficiency of search and the accuracy of results, a key vulnerable node discovery method based on Bayesian attack subgraphs and improved fuzzy C-means clustering is proposed. Firstly, the attack graph is divided into Bayesian attack subgraphs, and the analysis results of the complete attack graph are quickly obtained by aggregating the information of the attack path analysis in the subgraph to improve the time and space efficiency. Then, the actual threat features of the vulnerability nodes are extracted from the analysis results, and the threat features of the vulnerability itself in the common vulnerability scoring standard are considered to form the clustering features together. Next, the optimal number of clusters is adaptively adjusted according to the variance idea, and fuzzy clustering is performed based on the extracted clustering features. Finally, the key vulnerable nodes are determined by setting the feature priority. Experiments show that the proposed method can optimize the time and space efficiency of analysis, and the fuzzy clustering considering multiple features can improve the accuracy of analysis results.

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Section: Related Workmentioning

confidence: 99%