Bayesian Based Intrusion Detection System

Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 170)

Abstract

In this paper intrusion detection using Bayesian probability is discussed. The systems designed are trained a priori using a subset of the KDD dataset. The trained classifier is then tested using a larger subset of KDD dataset. Initially, a system was developed using a naive Bayesian classifier that is used to identify possible intrusions. This classifier was able to detect intrusion with an acceptable detection rate. The classier was then extended to a multi-layer Bayesian based intrusion detection. Finally, we introduce the concept that the best possible intrusion detection system is a layered approach using different techniques in each layer.

Keywords

Bayesian filter Intrusion detection KDD dataset Multi-layer filters Training engine U2R and R2L attacks 

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Computer Engineering DepartmentKing Saud UniversityRiyadhSaudi Arabia

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