BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection

• URL: http://arxiv.org/abs/2304.07226v1
• Date: Fri, 7 Apr 2023 09:42:07 GMT
• Title: BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection
• Authors: Yalu Wang, Zhijie Han, Jie Li, Xin He
• Abstract summary: This paper proposes a graph neural network algorithm based on behavior similarity (BS-GAT) using graph attention network. The results show that the proposed method is effective and has superior performance comparing to existing solutions.
• Score: 20.287285893803244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the development of the Internet of Things (IoT), network intrusion detection is becoming more complex and extensive. It is essential to investigate an intelligent, automated, and robust network intrusion detection method. Graph neural networks based network intrusion detection methods have been proposed. However, it still needs further studies because the graph construction method of the existing methods does not fully adapt to the characteristics of the practical network intrusion datasets. To address the above issue, this paper proposes a graph neural network algorithm based on behavior similarity (BS-GAT) using graph attention network. First, a novel graph construction method is developed using the behavior similarity by analyzing the characteristics of the practical datasets. The data flows are treated as nodes in the graph, and the behavior rules of nodes are used as edges in the graph, constructing a graph with a relatively uniform number of neighbors for each node. Then, the edge behavior relationship weights are incorporated into the graph attention network to utilize the relationship between data flows and the structure information of the graph, which is used to improve the performance of the network intrusion detection. Finally, experiments are conducted based on the latest datasets to evaluate the performance of the proposed behavior similarity based graph attention network for the network intrusion detection. The results show that the proposed method is effective and has superior performance comparing to existing solutions.

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