An AODE-based intrusion detection system for computer networks

Detecting anomalous traffic on the Internet has remained an issue of concern for the community of security researchers over the years. 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. Naive Bayes is a statistical inference learning algorithm with promise for document classification, spam detection and intrusion detection. The attribute independence issue associated with Naive Bayes has been resolved through the development of the Average One Dependence Estimator (AODE) algorithm. In this paper, we propose the application of AODE for intrusion detection. The performance of the proposed scheme is studied and analyzed on the KDD-99 intrusion benchmark data set. With a detection rate of 99.7%, AODE outperformed Naive Bayes, which reported a detection rate of 97.3%, and a larger number of false positives.