A Review on Feature Selection and Ensemble Techniques for Intrusion Detection System

Intrusion detection has drawn considerable interest as researchers endeavor to produce efficient models that offer high accuracy. Nevertheless, the challenge remains in developing reliable and Detection System (IDS) is capable of handling large amounts data, with trends evolving real-time circumstances. The design such a system relies on methods used, particularly feature selection techniques machine learning algorithms used. Thus motivated, this paper presents review ensemble used anomaly-based IDS research. Dimensionality reduction are reviewed, followed by categorization illustrate their effectiveness training phase detection. Selection most relevant features data been proven increase efficiency terms accuracy computational efficiency, hence its important role an IDS. We then analyze discuss variety IDS-based various (single classifier-based or ensemble-based), significance success intrusion area. Besides supervised unsupervised learning, combine several base one optimal predictive model improve performance consequently focuses employed illustrates how use improves models. Finally, laments open issues area offers research be considered designing IDSs.