Online and Adaptive Signature Learning for Intrusion Detection: An Application o
64,87 €
This thesis presents the case of dynamically and adaptively learning signatures for network intrusion detection using genetic based machine learning techniques. This is done by adaptively learning maximally general rules, which are referred to as signatures, from network traffic through a supervised learningclassifier system.
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