Intrusion Detection using Naive Bayes Classifier with Feature Reduction

Abstract

Intrusion detection is the process of monitoring and analyzing the events occurring in a computer system in order to detect signs of security problems. Today most of the intrusion detection approaches focused on the issues of feature selection or reduction, since some of the features are irrelevant and redundant which results lengthy detection process and degrades the performance of an intrusion detection system (IDS). The purpose of this study is to identify important reduced input features in building IDS that is computationally efficient and effective. For this we investigate the performance of three standard feature selection methods using Correlation-based Feature Selection, Information Gain and Gain Ratio. In this paper we propose method Feature Vitality Based Reduction Method, to identify important reduced input features. We apply one of the efficient classifier naive bayes on reduced datasets for intrusion detection. Empirical results show that selected reduced attributes give better performance to design IDS that is efficient and effective for network intrusion detection.