One-Dependence Estimators for Accurate Detection of Anomalous Network Traffic

Abstract

Classification of Internet traffic as anomalous has remained an issue of concern for the network security research community in recent times. Advances in computing performance, in terms of processing power and storage , have allowed the use of resource-intensive intelligent algorithms, to detect intrusive activities, in a timely manner. Na¨ıveNa¨ıve Bayes is a statistical inference learning algorithm with promise for document classification, spam detection and intrusion detection. The attribute independence issue associated with Na¨ıveNa¨ıve Bayes has been resolved through the development of one dependence estimation algorithms such as Super-Parent One Dependence Estimators (SPODE) and Average One Dependence Estimator (AODE). In this paper , we propose the design of an intrusion detection system based on the classification capabilities of SPODE and AODE for accurate detection of anomalous network traffic. The performance of the proposed scheme is studied and analyzed on the KDD-99 intrusion benchmark data set, with significantly high detection rates of the two algorithms as opposed to results obtained through Na¨ıveNa¨ıve Bayes simulation .