IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i7p1223-d218320.html
   My bibliography  Save this article

Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis

Author

Listed:
  • Jianlei Gao

    (School of Automation, Beijing Institute of Technology, Beijing 100081, China)

  • Senchun Chai

    (School of Automation, Beijing Institute of Technology, Beijing 100081, China)

  • Baihai Zhang

    (School of Automation, Beijing Institute of Technology, Beijing 100081, China)

  • Yuanqing Xia

    (School of Automation, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it would naturally be very meaningful to design an intrusion detection system (IDS) to effectively and quickly identify and detect malicious behaviors. In our work, we have proposed a method for an IDS-combined incremental extreme learning machine (I-ELM) with an adaptive principal component (A-PCA). In this method, the relevant features of network traffic are adaptively selected, where the best detection accuracy can then be obtained by I-ELM. We have used the NSL-KDD standard dataset and UNSW-NB15 standard dataset to evaluate the performance of our proposed method. Through analysis of the experimental results, we can see that our proposed method has better computation capacity, stronger generalization ability, and higher accuracy.

Suggested Citation

  • Jianlei Gao & Senchun Chai & Baihai Zhang & Yuanqing Xia, 2019. "Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis," Energies, MDPI, vol. 12(7), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1223-:d:218320
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/7/1223/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/7/1223/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kyung Choi & Xinyi Chen & Shi Li & Mihui Kim & Kijoon Chae & JungChan Na, 2012. "Intrusion Detection of NSM Based DoS Attacks Using Data Mining in Smart Grid," Energies, MDPI, vol. 5(10), pages 1-19, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ke Zhang & Zhi Hu & Yufei Zhan & Xiaofen Wang & Keyi Guo, 2020. "A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine," Energies, MDPI, vol. 13(18), pages 1-19, September.

    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. Ines Ortega-Fernandez & Francesco Liberati, 2023. "A Review of Denial of Service Attack and Mitigation in the Smart Grid Using Reinforcement Learning," Energies, MDPI, vol. 16(2), pages 1-15, January.
    2. Matthew Boeding & Kelly Boswell & Michael Hempel & Hamid Sharif & Juan Lopez & Kalyan Perumalla, 2022. "Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid," Energies, MDPI, vol. 15(22), pages 1-22, November.
    3. Neetesh Saxena & Bong Jun Choi, 2015. "State of the Art Authentication, Access Control, and Secure Integration in Smart Grid," Energies, MDPI, vol. 8(10), pages 1-33, October.

    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:jeners:v:12:y:2019:i:7:p:1223-:d:218320. 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.