About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Communications and Networking. 17th EAI International Conference, Chinacom 2022, Virtual Event, November 19-20, 2022, Proceedings

Research Article

Data Balancing Technique Based on AE-Flow Model for Network Instrusion Detection

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-34790-0_14,
        author={Xuanrui Xiong and Yufan Zhang and Huijun Zhang and Yi Chen and Hailing Fang and Wen Xu and Weiqing Lin and Yuan Zhang},
        title={Data Balancing Technique Based on AE-Flow Model for Network Instrusion Detection},
        proceedings={Communications and Networking. 17th EAI International Conference, Chinacom 2022, Virtual Event, November 19-20, 2022, Proceedings},
        proceedings_a={CHINACOM},
        year={2023},
        month={6},
        keywords={Imbalanced data Deep generative model-Flow AutoEncoder Network Intrusion Detection},
        doi={10.1007/978-3-031-34790-0_14}
    }
    
  • Xuanrui Xiong
    Yufan Zhang
    Huijun Zhang
    Yi Chen
    Hailing Fang
    Wen Xu
    Weiqing Lin
    Yuan Zhang
    Year: 2023
    Data Balancing Technique Based on AE-Flow Model for Network Instrusion Detection
    CHINACOM
    Springer
    DOI: 10.1007/978-3-031-34790-0_14
Xuanrui Xiong1, Yufan Zhang1,*, Huijun Zhang2, Yi Chen1, Hailing Fang1, Wen Xu1, Weiqing Lin1, Yuan Zhang3
  • 1: College of Communication and Information Engineering, Chongqing University of Posts and Telecommunications
  • 2: College of Environmental Resources, Chongqing Technology and Business University
  • 3: School of Computing, Chongqing Institute of Engineering
*Contact email: zhanghj@ctbu.edu.cn

Abstract

In network intrusion detection, the frequency of some rare network attacks is low, and such samples collected are relatively few. It results in an imbalanced proportion of each category in the dataset. Training the classifier with imbalanced datasets will bias the classifier to majority class samples and affect the classification performance on minority class samples. In response to this problem, researchers usually increase minority class samples and reduce majority class samples to get a balanced dataset. Therefore, we propose a data balancing technique based on AutoEncoder-Flow (AE-Flow) Model. Firstly, we use AutoEncoder (AE) to improve the deep generative model-Flow, obtaining AE-Flow. Then we use it to learn the distribution of minority class samples and generate new samples. Secondly, we use K-means and OneSidedSelection (OSS) algorithms to finish the undersampling of majority class samples. Finally we get a balanced dataset and use machine learning (ML) classifier to finish intrusion detection. We conducted comparative experiments on NSL-KDD dataset. The experimental results show that the balanced dataset obtained by our proposed method can effectively improve the Recall rate on minority class samples and the classification performance on overall samples.

Keywords
Imbalanced data Deep generative model-Flow AutoEncoder Network Intrusion Detection
Published
2023-06-10
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-34790-0_14
Copyright © 2022–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL