We believe that diversity and redundancy can help us prevent an attacker from hiding all of his or her traces. Therefore, we will strategically deploy diverse security monitors and build a set of techniques to combine information originating at the monitors. We have shown that we can formulate monitor deployment as a constrained optimization problem wherein the objective function is the utility of monitors in detecting intrusions. In this project, we will develop methods to select and place diverse monitors at different architectural levels in the system and evaluate the trustworthiness of the data generated by the monitors. We will build event aggregation and correlation algorithms to achieve inferences for intrusion detection. Those algorithms will combine the events and alerts generated by the deployed monitors with important system-related information, including information on the system architecture, users, and vulnerabilities. Since the rule-based detection systems fail to detect novel attacks, we will adapt and extend existing anomaly detection methods. We will build on our previous SoS-funded work that resulted in the development of the special-purpose intrusion detection methods.