IDEAS home Printed from https://ideas.repec.org/a/igg/jisp00/v18y2024i1p1-20.html
   My bibliography  Save this article

A Multi-Scale Feature Extraction Method Based on Improved Transformer for Intrusion Detection

Author

Listed:
  • Lijun Gu

    (School of Information Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou, China)

  • Zhongye Wang

    (General Road Campus Administrative Committee, Nanjing University of Aeronautics and Astronautics, Nanjing, China)

Abstract

Network traffic is a crucial indicator of network performance and network intrusions typically result in traffic anomalies. Capturing the differences and commonalities between different input features is challenging due to high-dimensional traffic data. To address this, we propose a multi-scale feature extraction method based on global additive attention (MSFE-GAA), which integrates time position information encoded by trigonometric functions to capture multi-scale temporal features. An improved Transformer with a similarity matrix captures the commonalities and differences, enhanced by global additive attention for long-term dependencies. Experiments on two public datasets show that the MSFE-GAA model outperforms other baseline models.

Suggested Citation

  • Lijun Gu & Zhongye Wang, 2024. "A Multi-Scale Feature Extraction Method Based on Improved Transformer for Intrusion Detection," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 18(1), pages 1-20, January.
    • Handle: RePEc:igg:jisp00:v:18:y:2024:i:1:p:1-20
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJISP.356893
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuan Tian & Wendong Wang & Jingyuan He, 2024. "An IIoT Temporal Data Anomaly Detection Method Combining Transformer and Adversarial Training," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 18(1), pages 1-28, January.
    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.

      More about this item

      Statistics

      Access and download statistics

      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:igg:jisp00:v:18:y:2024:i:1:p:1-20. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.