IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i13p4751-d850880.html
   My bibliography  Save this article

Borderline SMOTE Algorithm and Feature Selection-Based Network Anomalies Detection Strategy

Author

Listed:
  • Yong Sun

    (Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

  • Huakun Que

    (Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

  • Qianqian Cai

    (Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

  • Jingming Zhao

    (Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

  • Jingru Li

    (Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China)

  • Zhengmin Kong

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Shuai Wang

    (China Southern Power Grid Power Technology Co., Ltd., Guangzhou 510600, China)

Abstract

This paper proposes a novel network anomaly detection framework based on data balance and feature selection. Different from the previous binary classification of network intrusion, the network anomaly detection strategy proposed in this paper solves the problem of multiple classification of network intrusion. Regarding the common data imbalance of a network intrusion detection set, a resampling strategy generated by random sampling and Borderline SMOTE data is developed for data balance. According to the features of the intrusion detection dataset, feature selection is carried out based on information gain rate. Experiments are carried out on three basic machine learning algorithms (K-nearest neighbor algorithm (KNN), decision tree (DT), random forest (RF)), and the optimal feature selection scheme is obtained.

Suggested Citation

  • Yong Sun & Huakun Que & Qianqian Cai & Jingming Zhao & Jingru Li & Zhengmin Kong & Shuai Wang, 2022. "Borderline SMOTE Algorithm and Feature Selection-Based Network Anomalies Detection Strategy," Energies, MDPI, vol. 15(13), pages 1-13, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4751-:d:850880
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/13/4751/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/13/4751/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Changzhi Li & Dandan Liu & Mao Wang & Hanlin Wang & Shuai Xu, 2023. "Detection of Outliers in Time Series Power Data Based on Prediction Errors," Energies, MDPI, vol. 16(2), pages 1-19, January.

    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:15:y:2022:i:13:p:4751-:d:850880. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.