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BAE: Anomaly Detection Algorithm Based on Clustering and Autoencoder

Author

Listed:
  • Dongqi Wang

    (Software College, Northeastern University, Shenyang 110169, China)

  • Mingshuo Nie

    (Software College, Northeastern University, Shenyang 110169, China)

  • Dongming Chen

    (Software College, Northeastern University, Shenyang 110169, China)

Abstract

In this paper, we propose an outlier-detection algorithm for detecting network traffic anomalies based on a clustering algorithm and an autoencoder model. The BIRCH clustering algorithm is employed as the pre-algorithm of the autoencoder to pre-classify datasets with complex data distribution characteristics, while the autoencoder model is used to detect outliers based on a threshold. The proposed BIRCH-Autoencoder (BAE) algorithm has been tested on four network security datasets, KDDCUP99, UNSW-NB15, CICIDS2017, and NSL-KDD, and compared with representative algorithms. The BAE algorithm achieved average F-scores of 96.160, 81.132, and 91.424 on the KDDCUP99, UNSW-NB15, and CICIDS2017 datasets, respectively. These experimental results demonstrate that the proposed approach can effectively and accurately detect anomalous data.

Suggested Citation

  • Dongqi Wang & Mingshuo Nie & Dongming Chen, 2023. "BAE: Anomaly Detection Algorithm Based on Clustering and Autoencoder," Mathematics, MDPI, vol. 11(15), pages 1-14, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3398-:d:1210144
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    References listed on IDEAS

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    1. Yuancheng Li & Rixuan Qiu & Sitong Jing, 2018. "Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-16, February.
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    Cited by:

    1. Adel Binbusayyis, 2024. "Reinforcing Network Security: Network Attack Detection Using Random Grove Blend in Weighted MLP Layers," Mathematics, MDPI, vol. 12(11), pages 1-25, May.

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