Traditional anomaly-based Intrusion Detection Systems, relying on pattern matching and static signatures, are not really able to keep up with the creation of new forms of attacks, and particularly with zero-day attacks. In this talk we will analyze the problem, and present new types of misuse detection systems, based on unsupervised learning techniques, that can complement well traditional IDS systems and help detect zero-days techniques of attack and various other misbehaviours. A proof of concept based on our current research prototypes will be also presented.
Event: Black Hat USA 2004
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