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An AODE-based intrusion detection system for computer networks

conference contribution
posted on 2011-01-01, 00:00 authored by Zubair BaigZubair Baig, A S Shaheen, R Abdelaal
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. Naïve Bayes is a statistical inference learning algorithm with promise for document classification, spam detection and intrusion detection. The attribute independence issue associated with Naïve 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 Naïve Bayes, which reported a detection rate of 97.3%, and a larger number of false positives.

History

Event

Internet security. World congress (2011 : London, Eng.)

Pagination

28 - 35

Publisher

IEEE

Location

London, Eng.

Place of publication

Piscataway, N.J.

Start date

2011-02-21

End date

2011-02-23

ISBN-13

9780956426376

Language

eng

Publication classification

EN.1 Other conference paper

Title of proceedings

WorldCIS 2011 : Proceedings of the 2011 World Congress on Internet Security

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