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Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network

Author

Listed:
  • Xianer Ying

    (Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, China)

  • Mengshuang Pan

    (Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, China)

  • Xiner Chen

    (Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, China)

  • Yiyi Zhou

    (College of Letters & Science, University of California, Berkeley, Berkeley, CA 94720, USA)

  • Jianhua Liu

    (Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, China)

  • Dazhi Li

    (College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China)

  • Binghao Guo

    (Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, China)

  • Zihao Zhu

    (Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, China)

Abstract

The field of network security is highly concerned with intrusion detection, which safeguards the security of computer networks. The invention and application of intrusion detection technology play indispensable roles in network security, and it is crucial to investigate and comprehend this topic. Recently, with the continuous occurrence of intrusion incidents in virus propagation networks, traditional network detection algorithms for virus propagation have encountered limitations and have struggled to detect these incidents effectively and accurately. Therefore, updating the intrusion detection algorithm of the virus-spreading network is imperative. This paper introduces a novel system for virus propagation, whose core is a graph-based neural network. By organically combining two modules—a standardization module and a computation module—this system forms a powerful GNN model. The standardization module uses two methods, while the calculation module uses three methods. Through permutation and combination, we obtain six GNN models with different characteristics. To verify their performance, we conducted experiments on the selected datasets. The experimental results show that the proposed algorithm has excellent capabilities, high accuracy, reasonable complexity, and excellent stability in the intrusion detection of virus-spreading networks, making the network more secure and reliable.

Suggested Citation

  • Xianer Ying & Mengshuang Pan & Xiner Chen & Yiyi Zhou & Jianhua Liu & Dazhi Li & Binghao Guo & Zihao Zhu, 2024. "Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network," Mathematics, MDPI, vol. 12(10), pages 1-11, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1534-:d:1394627
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    References listed on IDEAS

    as
    1. Zhou, Jianlin & Li, Lingbo & Zeng, An & Fan, Ying & Di, Zengru, 2018. "Random walk on signed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 558-566.
    2. Xu, Xiao-Ke & Zhu, Jonathan J.H., 2016. "Flexible sampling large-scale social networks by self-adjustable random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 356-365.
    3. Sun, Lei & Liu, Tianyuan & Wang, Ding & Huang, Chengming & Xie, Yonghui, 2022. "Deep learning method based on graph neural network for performance prediction of supercritical CO2 power systems," Applied Energy, Elsevier, vol. 324(C).
    Full references (including those not matched with items on IDEAS)

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