On-line adaptive IDS scheme for detecting unknown network attacks using HMM models
An important problem in designing IDS schemes is an optimal trade-off between good detection and false alarm rate.
Specifically, in order to detect unknown network attacks, existing IDS schemes use anomaly detection which introduces a high false alarm rate. In this thesis we propose an IDS scheme based on overall behavior of the network. We capture the behavior with probabilistic models (HMM) and use only limited logic information about attacks. Once we set the detection rate to be high, we filter out false positives through stages. The key idea is to use probabilistic models so that even an unknown attack can be detected, as well as a variation of a previously known attack. The scheme is adaptive and real-time.
Simulation study showed that we can have a perfect detection of both known and unknown attacks while maintaining a very low false alarm rate.