Zhao, F.; Li, H.; Niu, K.; Shi, J.; Song, R. Application of Deep Learning-Based Intrusion Detection System (IDS) in Network Anomaly Traffic Detection. Applied and Computational Engineering 2024, 86, 250–256, doi:10.54254/2755-2721/86/20241604.
Zhao, F.; Li, H.; Niu, K.; Shi, J.; Song, R. Application of Deep Learning-Based Intrusion Detection System (IDS) in Network Anomaly Traffic Detection. Applied and Computational Engineering 2024, 86, 250–256, doi:10.54254/2755-2721/86/20241604.
Zhao, F.; Li, H.; Niu, K.; Shi, J.; Song, R. Application of Deep Learning-Based Intrusion Detection System (IDS) in Network Anomaly Traffic Detection. Applied and Computational Engineering 2024, 86, 250–256, doi:10.54254/2755-2721/86/20241604.
Zhao, F.; Li, H.; Niu, K.; Shi, J.; Song, R. Application of Deep Learning-Based Intrusion Detection System (IDS) in Network Anomaly Traffic Detection. Applied and Computational Engineering 2024, 86, 250–256, doi:10.54254/2755-2721/86/20241604.
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, through the review of 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 effectively improve abnormal traffic detection's accuracy and efficiency. 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
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
