A Knowledge-Based Approach to Intrusion Detection Modeling
Current state of the art Intrusion Detection and Prevention Systems (IDPS) are signature-based systems that detect threats and vulnerabilities by cross-referencing the threat or vulnerability signatures in their databases. These systems are incapable of taking advantage of heterogeneous data sources for analysis of system activities for threat detection. This paper presents a situation-aware intrusion detection model that integrates these heterogeneous data sources and build a semantically rich knowledge-base to detect cyber threats/vulnerabilities. Cyber crimes are being used to assist activities like espionage, politically motivated attacks and credit card fraud at an alarming rate.