The timely and reliable data transfer required by many networked applications necessitates the development of comprehensive security solutions to monitor and protect against an increasing number of malicious attacks. However, providing complete cyber space situation awareness is extremely challenging because of the lack of effective translation mechanisms from low-level situation information to high-level human cognition for decision making and action support. We propose an adaptive cyber security monitoring system that integrates a number of component techniques to collect timeseries situation information, perform intrusion detection, keep track of event evolution, characterize and identify security events, and present a visual representation in order to provide comprehensive situational view so that corresponding defense actions can be taken in a timely and effective manner. We explore the principles of designing and applying appropriate visualization techniques for situation monitoring by defining graphical representations of security events. This differs from the traditional rule-based pattern matching techniques in that 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. The events and corresponding event types are visualized using a stemplot to show location and quantity. Extensive simulation results on event identification illustrate the efficacy of the proposed system.