Statistical methods for feature selection: unlocking the key to improved accuracy

Abstract

The ever-growing amount of data generated by modern networks poses significant challenges for intrusion detection systems (IDS) in effectively analyzing and classifying security risks. Therefore, it is crucial to identify the most biased characteristics for building efficient and effective IDS algorithms. However, not all features are equally informative or relevant for intrusion detection. In response to these problems, this study proposes a Hybrid approach that uses traditional and advanced statistical techniques. The proposed method effectively validates the features generated from the hybrid model and set-operation theorem to provide the best optimal subset of features for IDS. Various machine learning methods are used to test the proposed model on three popular IDS datasets: NSL-KDD, UNSW NB15, and CIC-DDoS2019. The experimental findings show that the suggested hybrid technique improves IDS performance effectively and efficiently, providing a viable answer to the issues that intrusion detection systems confront.