Intrusion detection model based on genetic algorithm optimization extreme learning machine of K-fold stratified cross-validation

Chen Chen; Xiaopeng Shi; Xiaoyan Ye; Lintao Yang

doi:10.1117/12.2673803

6 April 2023 Intrusion detection model based on genetic algorithm optimization extreme learning machine of K-fold stratified cross-validation

Chen Chen, Xiaopeng Shi, Xiaoyan Ye, Lintao Yang

Author Affiliations +

Proceedings Volume 12615, International Conference on Signal Processing and Communication Technology (SPCT 2022); 126152G (2023) https://doi.org/10.1117/12.2673803
Event: International Conference on Signal Processing and Communication Technology (SPCT 2022), 2022, Harbin, China

Abstract

Aiming at the problem of random generation of Extreme Learning Machine (ELM) parameters, an intrusion detection model based on GA-ELM of K-fold stratified cross-validation is proposed. Genetic Algorithm (GA) is used to optimize the parameters of ELM. The simulation experiment is carried out on NSL-KDD data set, and the GA-ELM model is trained by K-fold stratified cross-validation method. The experimental results show that the proposed model has a higher detection rate than ELM, GA-ELM and GA-ELM of nonstratified cross-validation.

Citation Download Citation

Chen Chen, Xiaopeng Shi, Xiaoyan Ye, and Lintao Yang "Intrusion detection model based on genetic algorithm optimization extreme learning machine of K-fold stratified cross-validation", Proc. SPIE 12615, International Conference on Signal Processing and Communication Technology (SPCT 2022), 126152G (6 April 2023); https://doi.org/10.1117/12.2673803

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