Improved Transformer-Conv-BiLSTM-based method for intrusion detection

Qinhao Li; Chunlin Huang

doi:10.1117/12.3028999

22 May 2024 Improved Transformer-Conv-BiLSTM-based method for intrusion detection

Qinhao Li, Chunlin Huang

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131762U (2024) https://doi.org/10.1117/12.3028999
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Due to the continuous expansion of the Internet scale, network traffic has experienced an explosive growth, accompanied by increasingly complex structures. Improving the detection accuracy of malicious traffic and efficiently distinguishing different categories of malicious traffic has become an urgent problem to be addressed. Research has shown that hybrid approaches combining CNN and BiLSTM exhibit strong responsiveness and perform well in solving research problems such as video classification, sentiment analysis, and emotion recognition. Therefore, in order to enhance the learning capability and detection performance of IDS, this paper proposes an improved version of an intrusion detection method based on the Transformer and Conv-BiLSTM networks. This model combines the advantages of both modules to improve performance compared to traditional models.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qinhao Li and Chunlin Huang "Improved Transformer-Conv-BiLSTM-based method for intrusion detection", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131762U (22 May 2024); https://doi.org/10.1117/12.3028999

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