The input characteristics of a real-time IDS change continuously with time therefore setting a rigid (time and behavior invariant) classification threshold limits the accuracy that the IDS can potentially achieve. A generic threshold tuning method and system is proposed which can adaptively tune the detection threshold of a real-time IDS in accordance with varying host and network behavior. The method and system perform statistical and information-theoretic analysis of network and host-based IDSs' anomaly based intrusions to reveal a consistent time correlation structure between benign activity periods which is used to predict future anomaly scores and to adapt an IDS' detection threshold accordingly.