Performance evaluation of intrusion detection system using classifier ensembles. (2017) - Tama, Bayu Adhi; Rhee, Kyung-Hyune
An intrusion detection system (IDS) plays a critical role in computer protection systems. Numerous approaches such as machine learning, data mining, and statistical techniques have been examined for IDS task. Recent studies reveal that combining multiple classifiers, i.e., classifiers ensemble, may possess better performance compared to single classifier. In this paper, we conduct a comparative study of the performance of five renowned ensemble techniques, i.e., bagging, stacking, boosting, rotation forest, and voting, based on three base classifiers, i.e., decision tree (C4.5), convolutional neural network (CNN), and support vector machine (SVM). Based on the experimental results, boosting and stacking perform better than bagging, rotation forest, and voting scheme. In particular, boosting-C4.5 and stacking possess the best performance in terms of performance metrics such as accuracy, precision, recall, and AUC value.