Adaptive network intrusion detection system using a hybrid approach

Abstract

Any activity aimed at disrupting a service or making a resource unavailable or gaining unauthorized access can be termed as an intrusion. Examples include buffer overflow attacks, flooding attacks, system break-ins, etc. Intrusion detection systems (IDSs) play a key role in detecting such malicious activities and enable administrators in securing network systems. Two key criteria should be met by an IDS for it to be effective: (i) ability to detect unknown attack types, (ii) having very less miss classification rate. In this paper we describe an adaptive network intrusion detection system, that uses a two stage architecture. In the first stage a probabilistic classifier is used to detect potential anomalies in the traffic. In the second stage a HMM based traffic model is used to narrow down the potential attack IP addresses. Various design choices that were made to make this system practical and difficulties faced in integrating with existing models are also described. We show that this system achieves good performance empirically.