A survey of intrusion detection systems based on ensemble and hybrid classifiers.

Due to the frequency of malicious network activities and network policy violations, intrusion detection systems (IDSs) have emerged as a group of methods that combats the unauthorized use of a network's resources. Recent advances in information technology have produced a wide variety of machine learning methods, which can be integrated into an IDS. This study presents an overview of intrusion classification algorithms, based on popular methods in the field of machine learning. Specifically, various ensemble and hybrid techniques were examined, considering both homogeneous and heterogeneous types of ensemble methods. In addition, special attention was paid to those ensemble methods that are based on voting techniques, as those methods are the simplest to implement and generally produce favorable results. A survey of recent literature shows that hybrid methods, where feature selection or a feature reduction component is combined with a single-stage classifier, have become commonplace. Therefore, the scope of this study has been expanded to encompass hybrid classifiers.