By Topic

DIDFAST.BN: Distributed Intrusion Detection And Forecasting Multiagent 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)
Jemili, F. ; ENSI, Manouba Univ. ; Zaghdoud, M. ; Ben Ahmed, M.

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

Published in:

Information and Communication Technologies, 2006. ICTTA '06. 2nd  (Volume:2 )

Date of Conference:

0-0 0