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Publication

Attacker Intention Discovery Layer for Intrusion Detection Systems Using Hidden Markov Models

Koskei, Jordan Kiprop
Abstract
Currently deployed intrusion detection systems (IDS) have no capacity to discover attacker high level intentions. Understanding an intruder's intention greatly enhances network security as it allows deployment of more accurate pre-emptive counter-measures and better disaster recovery. In this thesis, we propose a system where we model a known attack scenario using HMM and use alerts from an IDS later to discover an attackers set of intentions for a given set of alerts.
Date
2011-07-01
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