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A High Performance Intrusion Detection System Using LightGBM Based on Oversampling and Undersampling

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Intelligent Computing Theories and Application (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13393))

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Abstract

Intrusion detection system plays an important role in network security, however, the problem with data imbalance limits the detection ability of intrusion detection system. In order to improve the performance of intrusion detection system, this paper proposes to use the adaptive synthetic sampling technique (ADASYN) and random under sampling technique to alleviate the problem of data imbalance in intrusion detection. Firstly, the majority class samples in the dataset are removed by undersampling technology and the minority class samples are oversampled, so the samples can reach a balanced state. Subsequently, a sparse autoencoder (SAE) extracts features from the resampled data to fit the original sample as closely as possible. Finally, LightGBM is applied on the processed dataset for the classification process. Multi-classification experiments were conducted on KDD99 and UNSWNB15 datasets. We compare six models’ performance and find LightGBM is superior to other models. Furthermore, we also compare existing methods and the results show that our proposed method outperforms current methods.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China under Grant 61862007, and Guangxi Natural Science Foundation under Grant 2020GXNSFBA297103.

Author information

Authors and Affiliations

  1. School of Artificial Intelligence, Guangxi Minzu University, Nanning, China

    Hao Zhang, Lina Ge & Zhe Wang

  2. Key Laboratory of Network Communication Engineering, Guangxi Minzu University, Nanning, China

    Hao Zhang, Lina Ge & Zhe Wang

  3. Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning, China

    Zhe Wang

Authors
  1. Hao Zhang
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  2. Lina Ge
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  3. Zhe Wang
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Corresponding author

Correspondence to Lina Ge .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi'an Polytechnic University, Xi'an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Zhang, H., Ge, L., Wang, Z. (2022). A High Performance Intrusion Detection System Using LightGBM Based on Oversampling and Undersampling. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_53

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  • DOI: https://doi.org/10.1007/978-3-031-13870-6_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13869-0

  • Online ISBN: 978-3-031-13870-6

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