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An Ensemble Feature Selection Algorithm for Machine Learning based Intrusion Detection System

In recent years, we have witnessed the significant growth of the Internet along with emerging security threats. A machine learning-based Intrusion Detection System (IDS) is widely employed to detect cyber attacks by continuously monitoring network traffic. However, the diversity of network features considerably affected the accuracy and training time of the IDS model. In this paper, a lightweight and effective feature selection algorithm for IDS is proposed. This algorithm combines the advantages of both Random Forest and AdaBoost algorithms. The evaluation results on popular datasets (NSL- KDD, UNSW-NB15, and CICIDS-2017) show that our proposal outperforms existing feature selection algorithms regarding the detection accuracy and the number of selected features.