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Application of deep learning-based Intrusion Detection System (IDS) in network anomaly traffic detection
- 1 Computer Science, Stevens Institute of Technology, NJ, USA
- 2 Computer Engineering, New York University, New York, USA
- 3 Artificial intelligence, Royal Holloway University of London, Egham, UK
- 4 Computer Science, University of Electronic Science and Technology of China, Cheng Du, China
- 5 Information System & Technology Data Analytics, California State University, CA, USA
* Author to whom correspondence should be addressed.
Abstract
This study discusses the application of deep learning technology in network intrusion detection systems (IDS) and focuses on a new model named CNN-Focal. First, reviewing traditional IDS technology, it analyzes its limitations in dealing with complex network traffic. Then, the design principle of the CNN-Focal model is described in detail, which uses threshold convolution and SoftMax multi-class classification technology to improve abnormal traffic detection’s accuracy and efficiency effectively. The experimental results show that CNN-Focal performs well on the open data set, demonstrating the potential and advantages of its application in the natural network environment and providing a new perspective and method for further research of deep learning in the field of network security in the future.
Keywords
Deep learning, Intrusion detection System (IDS), CNN-Focal Model, Abnormal network traffic
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Cite this article
Zhao,F.;Li,H.;Niu,K.;Shi,J.;Song,R. (2024). Application of deep learning-based Intrusion Detection System (IDS) in network anomaly traffic detection. Applied and Computational Engineering,86,231-237.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 6th International Conference on Computing and Data Science
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