monitoring security events using integrated correlation-based techniques
We propose an adaptive cyber security monitoring system that integrates a number of component techniques to collect time-series situation information, perform intrusion detection, and characterize and identify security events so corresponding defense actions can be taken in a timely and effective manner. We employ a decision fusion algorithm with analytically proven performance guarantee for intrusion detection based on local votes from distributed sensors. The security events in the proposed system are represented as forms of correlation networks using random matrix theory and identified through the computation of network similarity measurement. Extensive simulation results on event identification illustrate the efficacy of the proposed system.
cyber security random matrix theory event correlation