A Neural Network Ensemble Classifier for Effective Intrusion Detection Using Fuzzy Clustering and Radial Basis Function Networks

Abstract

Intrusion Detection Systems have considerable importance in preventing security threats and protecting computer networks against attackers. So far, various classification approaches using data mining and machine learning techniques have been proposed to the problem of intrusion detection. However, using single classifier systems for intrusion detection suffers from some limitations including lower detection rate for low-frequent attacks, detection instability, and complexity in training process. Ensemble classifier systems combine several individual classifiers and obtain a classifier with higher performance. In this paper, we propose a new ensemble classifier using Radial Basis Function (RBF) neural networks and fuzzy clustering in order to increase detection accuracy and stability, reduce false positives, and provide higher detection rate for low-frequent attacks. We also use a hybrid combination method to aggregate the individual predictions of the base classifiers, which helps to increase detection accuracy. The experimental results on NSL-KDD data set demonstrate that our proposed system has a higher detection accuracy compared to other wellknown classification systems. It also performs more effectively for detection of low-frequent attacks. Furthermore, the proposed ensemble method offers better performance compared to popular ensemble methods.