Intrusion detection based on “Hybrid” propagation in Bayesian Networks

Abstract

The goal of a network-based intrusion detection system (IDS) is to identify malicious behaviour 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 buidl automatic intrusion detection system based on signature recognition. A major difficulty of this system is that the uncertainty on parameters can have two origins. The first source of uncertainty