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This paper proposes an adaptive cyber security monitoring system that integrates a number of component techniques to collect time-series situation information, perform intrusion detection, keep track of event evolution, and characterize and identify security events so corresponding defense actions can be taken in a timely and effective manner. Particularly, the authors employ a decision fusion algorithm with analytically proven performance guarantee for intrusion detection based on local votes from distributed sensors. Different from the traditional rule-based pattern matching technique, security events in the proposed system are represented in a graphical form of correlation networks using random matrix theory and identified through the computation of network similarity measurement.
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