Malware Prediction for Situational Understanding and Preemptive Cyber Defense

BlueRISC's proposed solution provides a fundamentally new approach to predicting the presence of malware in a network based on a novel graph-theoretical framework. Unlike traditional approaches that are reactive, it builds on a predictive capability that is flexible, adaptive, and is not relying on signatures or strict rule based malware definitions. The approach captures system motion as a predictive surrogate for malicious activity. This occurs based a concise graph-based forensics representation of a systems state and associated space-time correlations algorithms which use graph theory.