An attention-based automatic vulnerability detection approach with GGNN

Tang, Gaigai; Yang, Lin; Zhang, Long; Cao, Weipeng; Meng, Lianxiao; He, Hongbin; Kuang, Hongyu; Yang, Feng; Wang, Huiqiang

doi:10.1007/s13042-023-01824-7

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…At present, there is much research on key vulnerability search. Hao et al [24] and Tang et al [25] train neural network models to identify key vulnerabilities on network devices using static analysis, but this method is only applicable to a single device and cannot be dynamically combined with other devices in the network. Li et al [26] use the Kemeny constant as a global connectivity measure to identify network key connections and network decomposition is used to cut off connections to minimize global connectivity measures, thereby obtaining key vulnerabilities.…”

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%