Intrusion detection based on "hybrid" propagation in Bayesian Networks

Authors:
Farah Jemili;Montaceur Zaghdoud;Mohamed Ben Ahmed
Affiliations:
Laboratorie RIADI, ENSI, Manouba University, Manouba, Tunisia;Laboratorie RIADI, ENSI, Manouba University, Manouba, Tunisia;Laboratorie RIADI, ENSI, Manouba University, Manouba, Tunisia
Venue:
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Year:
2009

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Abstract

The goal of a network-based intrusion detection system (IDS) is to identify malicious behavior that targets a network and its resources. Intrusion detedion 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. A major difficulty of this system is that the uncertainty on parameters can haw two origins. The first source of uncertainty comes from the uncertain character of information due to a natural variability resulting from stochastic phenomena. The second source of uncertainty is related to the Imprecise and incomplete character of information due to a lack of knowledge. The goal of this work is to propose a method to propagate both the stochastic and tbe epistemic uncertainties, coming respectively from the uncertain and imprccise character of information, through the bayesian model, in an intrusion detection context.