Abstract: Intrusion detection,as a security defense technique to protect the network from attacks,plays an important role in the field of network security.Researchers have proposed different network intrusion detection models using machine learning techniques.However,the problems of feature redundancy and machine learning parameter optimization are still challenges for intrusion detection systems.Existing studies considerthe two as independent problems and optimized them separately.However,the machine learning parameters are closely related to the features in the training data,and changes in the feature set are likely to cause changes in the optimal machine learning parameters.To address this problem,an intrusion detection method based on combined optimization of improved pigeon flocking algorithm(ICOPIO)is proposed.It can simultaneously achieve feature screening and machine learning parameter optimization,avoiding the interference of human parameter settings,reducing the influence of redundant and irrelevant features,and further improving the performance of the intrusion detection model.In addition,Spark is used to parallelize ICOPIO to improve the efficiency of ICOPIO.Finally,two intrusion detection standard datasets,NSL-KDD and UNSW-NB15,are used to evaluate the model,and by comparing with several existing related methods,the proposed model achieves the best results in the evaluation metrics of TPR,FPR,and average accuracy,and it proves that ICOPIO has good scalability.

Key words: Feature selection, Parameter optimization, Intrusion detection, Parallelization, Pigeon swarm algorithm