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

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.