By Topic

A Framework for an Adaptive Intrusion Detection System using Bayesian Network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Farah Jemili ; RIADI Laboratory, ENSI, Manouba University, Manouba 2010, Tunisia ; Montaceur Zaghdoud ; Mohamed Ben Ahmed

The goal of a network-based intrusion detection system (IDS) is to identify malicious behavior that targets a network and its resources. Intrusion detection parameters are numerous and in many cases they present uncertain and imprecise causal relationships which can affect attack types. A Bayesian Network (BN) is known as graphical modeling tool used to model decision problems containing uncertainty. In this paper, a BN is used to build automatic intrusion detection system based on signature recognition. The goal is to recognize signatures of known attacks, match the observed behavior with those known signatures, and signal intrusion when there is a match. A major difficulty of this system is that intrusions signatures change over the time and the system must be retrained. An IDS must be able to adapt to these changes. The goal of this paper is to provide a framework for an adaptive intrusion detection system that uses Bayesian network.

Published in:

Intelligence and Security Informatics, 2007 IEEE

Date of Conference:

23-24 May 2007