DIDFAST.BN: Distributed Intrusion Detection And Forecasting Multiagent System using Bayesian Network

This paper proposes a distributed intrusion detection and forecasting multiagent system using Bayesian network. System architecture is composed by two interconnected layers of intelligent agents. The first layer is concerned by intrusion detection. On each host of a distributed computers system, an intelligent agent using Bayesian network is charged by detecting intrusion eventuality. The second layer is based upon one intelligent agent which is charged by intrusion forecasting task based on Bayesian network prediction. Agents of these two layers communicate using messages. When new intrusion is detected on the first layer, the agent responsible of this host informs the forecasting agent placed in the second layer. This latter computes conditional probabilities of intrusion appearance on each host of the distributed system, and informs the administrator of the concerned host about possible ultimate intrusion