Research on Optimization of Network Intrusion Detection Algorithm Based on Deep Learning

Yijun Han ()
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Yijun Han: Shanxi College of Applied Science and Technology

A chapter in Proceedings of 2024 6th International Conference on Economic Management and Cultural Industry (ICEMCI 2024), 2025, pp 139-145 from Springer

Abstract: Abstract This study explores the application of deep learning algorithms in network intrusion detection by optimizing CNN and RNN models to improve their detection rate and reduce false alarm rates on the KDD Cup 99 and CICIDS2017 datasets. The experimental results show that the optimized CNN achieved a detection rate of 94% and reduced the false alarm rate to 1.5%. The RNN’s detection rate increased to 94.5%, with a false alarm rate reduced to 2%. The findings confirm the effectiveness of deep learning models in handling complex attacks and demonstrate the significant performance improvement brought by optimization strategies, providing more accurate detection methods for network security.

Keywords: deep learning; network intrusion detection; model optimization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-642-0_15

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DOI: 10.2991/978-94-6463-642-0_15

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