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

DOI

10.2991/978-94-6463-642-0_15How to use a DOI?

Keywords

deep learning; network intrusion detection; model optimization

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.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yijun Han
PY  - 2025
DA  - 2025/01/24
TI  - Research on Optimization of Network Intrusion Detection Algorithm Based on Deep Learning
BT  - Proceedings of 2024 6th International Conference on Economic Management and Cultural Industry (ICEMCI 2024)
PB  - Atlantis Press
SP  - 139
EP  - 145
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-642-0_15
DO  - 10.2991/978-94-6463-642-0_15
ID  - Han2025
ER  -