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2 December 2022 Research on attack behaviour detection based on dynamic graph neural network in power IoT system
Xingshen Wei, Peng Gao, Junxian Xu, Haotian Zhang, Qiuhan Tian, Zengzhou Ma
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Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 122881K (2022) https://doi.org/10.1117/12.2640979
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China
Abstract
The network attacks targeted at the power system become much more complex and covert. However, traditional security protection methods cannot well detect such unknown attacks or multi-step attacks, which leads to a constant threat. How to use the massive log and warning data generated from various types of traditional security equipment to effectively detect and trace the advanced network threats becomes more and more important. "Graph" is a data structure that can better represent the power network system and thus enforcing correlation analysis on the network graph is an important detection method. This paper first reviews the graph construction methods and graph representation learning method. Furthermore Dynamic-Graph Neural Network (DGNN) based method to detect attack behavior is proposed. The experimental evaluation shows that DGNN based method can achieve better performance compared with both shallow embedding method and static-GNN method.
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Xingshen Wei, Peng Gao, Junxian Xu, Haotian Zhang, Qiuhan Tian, and Zengzhou Ma "Research on attack behaviour detection based on dynamic graph neural network in power IoT system", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 122881K (2 December 2022); https://doi.org/10.1117/12.2640979
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KEYWORDS
Neural networks
Network security
Information security
Machine learning
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