Performance evaluation of intrusion detection system using classifier ensembles. (2017)

• Record Type: Journal Article
• Authors: Tama, Bayu Adhi; Rhee, Kyung-Hyune
• Is Part Of: International journal of internet protocol technology. Volume 10:Number 1 (2017)
• Page Start: 22
• Page End: 29
• Publication Date: 2017
• Subjects: intrusion detection systems -- IDS -- ensemble classification -- performance indicators -- data security -- bagging -- stacking -- boosting -- rotation forest -- voting -- decision tree -- convolutional neural networks -- CNNs -- support vector machines -- SVM; File Transfer Protocol (Computer network protocol) -- Periodicals; Multicasting (Computer networks) -- Periodicals; 004.678
• Languages: English
• ISSNs: 1743-8209

Abstract

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.