Hybrid Intrusion Detection and Prediction multiAgent System HIDPAS
Abstract
This paper proposes an intrusion detection and prediction system based on uncertain and imprecise inference networks and its implementation. Giving a historic of sessions, it is about proposing a method of supervised learning doubled of a classifier permitting to extract the necessary knowledge in order to identify the presence or not of an intrusion in a session and in the positive case to recognize its type and to predict the possible intrusions that will follow it. The proposed system takes into account the uncertainty and imprecision that can affect the statistical data of the historic. The systematic utilization of an unique probability distribution to represent this type of knowledge supposes a too rich subjective information and risk to be in part arbitrary. One of the first objectives of this work was therefore to permit the consistency between the manner of which we represent information and information which we really dispose.
Cite
@article{arxiv.0909.4889,
title = {Hybrid Intrusion Detection and Prediction multiAgent System HIDPAS},
author = {Farah Jemili and Montaceur Zaghdoud and Mohamed Ben Ahmed},
journal= {arXiv preprint arXiv:0909.4889},
year = {2009}
}
Comments
10 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact Factor 0.423, http://sites.google.com/site/ijcsis/