IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i10p1534-d1394627.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/10/1534/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/10/1534/
    Download Restriction: no
    ---><---

    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xu, Xiao-Ke & Wang, Xue & Xiao, Jing, 2018. "Inferring parent–child relationships by a node-remove centrality framework in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 222-232.
    2. Wu, Zheng & Zhang, Yue & Dong, Ze, 2024. "NOx concentration prediction based on multi-channel fused spectral temporal graph neural network in coal-fired power plants," Energy, Elsevier, vol. 305(C).
    3. Li, Lingbo & Fan, Ying & Zeng, An & Di, Zengru, 2019. "Binary opinion dynamics on signed networks based on Ising model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 433-442.
    4. Du, Yadong & Yang, Ce & Zhao, Ben & Hu, Chenxing & Zhang, Hanzhi & Yu, Zhiyi & Gao, Jianbing & Zhao, Wei & Wang, Haimei, 2023. "Optimal design of a supercritical carbon dioxide recompression cycle using deep neural network and data mining techniques," Energy, Elsevier, vol. 271(C).
    5. Li, Ai-Wen & Xu, Xiao-Ke & Fan, Ying, 2022. "Immunization strategies for false information spreading on signed social networks," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    6. Tofighy, Sajjad & Charkari, Nasrollah Moghadam & Ghaderi, Foad, 2022. "Link prediction in multiplex networks using intralayer probabilistic distance and interlayer co-evolving factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    7. Fuentes, Emilio Aced & Santini, Simone, 2021. "Network navigation with non-Lèvy superdiffusive random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    8. Gao, Meng & Ge, Ruijun, 2024. "Mapping time series into signed networks via horizontal visibility graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    9. Yan, Chao & Chang, Zhenhai, 2020. "Modularized convex nonnegative matrix factorization for community detection in signed and unsigned networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    10. Boussaid, Taha & Rousset, François & Scuturici, Vasile-Marian & Clausse, Marc, 2024. "Enabling fast prediction of district heating networks transients via a physics-guided graph neural network," Applied Energy, Elsevier, vol. 370(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1534-:d:1394627. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.