Mohammad Amini - Department of Information Technology, University of Qom, Qom, Iran
Jalal Rezaeenoor - Department of Information Technology, University of Qom, Qom, Iran
Esmaeil Hadavandi - Department of Information Technology, University of Qom, Qom, Iran

The application of data mining techniques in intrusion detection has attracted considerable attention from the research community. Ensemble learning can be used as an effective classification technique for intrusion detection. In an ensemble classification system, different base classifiers are combined in order to obtain a classifier with higher performance. In this paper, the most successful ensemble techniques used in the field of intrusion detection are introduced and discussed. These ensemble techniques are categorized based on the main idea of the ensemble and similarities in implementation of the models. The goal of this review is to provide insight into the benefits of current ensemble methods and how they can increase the performance of intrusion detection.

Intrusion Detection, Data mining, Ensemble Classifier, Performance Measures