Hybrid Intrusion Detection and Prediction multiAgent System HIDPAS

TL;DR

This paper introduces a hybrid multi-agent intrusion detection and prediction system that employs uncertain inference networks to handle imprecise data, aiming to improve detection accuracy and predict future intrusions.

Contribution

It presents a novel supervised learning method using uncertain inference networks within a multi-agent system for intrusion detection and prediction, addressing data uncertainty and imprecision.

Findings

Effective detection of intrusions in sessions

Ability to recognize intrusion types

Prediction of subsequent intrusions

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.