An Intrusion Detection System based on PSO-GWO Hybrid Optimized Support Vector Machine

Intrusion Detection System (IDS) is an important tool to ensure network security, which can detect and prevent malicious behavior in time. However, the noise and redundancy of data often reduce the detection performance of classifiers. The traditional model of intrusion detection system cannot effectively solve this problem. Therefore, in this paper, autoencoders (AEs) are firstly used to reduce the dimension of the original data, and a hybrid model combining particle swarm optimization (PSO) and gray wolf optimization (GWO) is proposed to optimize the support vector machine (SVM) parameters. This method combines the two optimization algorithms and selects the optimal parameter values according to the locally enhanced particles to train the classifier. In this paper, the NSL-KDD benchmark dataset and UNSW-NB15 dataset are used to evaluate the proposed model, and the model is compared with other classification methods separately. The experimental results show that our hybrid optimization model has better performance in detection accuracy and provides good detection rate and false alarm rate.