Abstract
Intrusion detection is one of the important means to ensure network security, aiming at the problem that the current network intrusion detection model cannot obtain the ideal network intrusion detection effect, a large-scale network intrusion detection model based on FS feature selection is designed. This study takes the NSL-KDD dataset as an example, and performs numerical and normalization processing on it. The main features are selected using the grey wolf optimization algorithm fused with cuckoo search. Taking the feature as input, the C4.5 algorithm is used to realize network intrusion detection. The results show that under the application of the constructed model, the accuracy rate (A), detection rate (D), and precision rate (P) are all greater than 90%, and the F1 score is greater than 1, indicating that the model has a good performance in network intrusion detection.
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Aknowledgement
1. General project of Jilin Provincial Department of Education: Detection Model of Network Intrusion Based on FS Feature Selection and Extreme Learning Machine (jjkh20210615kj).
2. General project of Jilin Provincial Department of Education: Building Intelligent Data Analysis Platforms to Help the Operation and Management of Insurance Enterprises (jjkh20220598kj).
3. General project of Jilin Provincial Department of Education: Research on Robot Bending Personalized Orthodontic Arch Wire Forming (jjkh20200561kj).
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Hong, M., Zou, Y., Ai, C. (2023). Large Scale Network Intrusion Detection Model Based on FS Feature Selection. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13655. Springer, Cham. https://doi.org/10.1007/978-3-031-20096-0_29
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DOI: https://doi.org/10.1007/978-3-031-20096-0_29
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