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Efficient Large-Scale IoT Botnet Detection through GraphSAINT-Based Subgraph Sampling and Graph Isomorphism Network

Author

Listed:
  • Lihua Yin

    (Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China)

  • Weizhe Chen

    (Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China)

  • Xi Luo

    (Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China)

  • Hongyu Yang

    (Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China)

Abstract

In recent years, with the rapid development of the Internet of Things, large-scale botnet attacks have occurred frequently and have become an important challenge to network security. As artificial intelligence technology continues to evolve, intelligent detection solutions for botnets are constantly emerging. Although graph neural networks are widely used for botnet detection, directly handling large-scale botnet data becomes inefficient and challenging as the number of infected hosts increases and the network scale expands. Especially in the process of node level learning and inference, a large number of nodes and edges need to be processed, leading to a significant increase in computational complexity and posing new challenges to network security. This paper presents a novel approach that can accurately identify diverse intricate botnet architectures in extensive IoT networks based on the aforementioned circumstance. By utilizing GraphSAINT to process large-scale IoT botnet graph data, efficient and unbiased subgraph sampling has been achieved. In addition, a solution with enhanced information representation capability has been developed based on the Graph Isomorphism Network (GIN) for botnet detection. Compared with the five currently popular graph neural network (GNN) models, our approach has been tested on C2, P2P, and Chord datasets, and higher accuracy has been achieved.

Suggested Citation

  • Lihua Yin & Weizhe Chen & Xi Luo & Hongyu Yang, 2024. "Efficient Large-Scale IoT Botnet Detection through GraphSAINT-Based Subgraph Sampling and Graph Isomorphism Network," Mathematics, MDPI, vol. 12(9), pages 1-20, April.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1315-:d:1383082
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    References listed on IDEAS

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    1. Ying Xing & Hui Shu & Hao Zhao & Dannong Li & Li Guo, 2021. "Survey on Botnet Detection Techniques: Classification, Methods, and Evaluation," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-24, April.
    2. Ruidong Chen & Weina Niu & Xiaosong Zhang & Zhongliu Zhuo & Fengmao Lv, 2017. "An Effective Conversation-Based Botnet Detection Method," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-9, April.
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