CyStAR: Cyber-Physical Threat Detection and Asset Monitoring

CyStAR aims to provide a solution for identifying suspicious behavior in cyber-physical systems through the use of machine learning anomaly detection. The target customer is cybersecurity vendors that focus on industrial control systems (ICS) environments to reduce false positives and negatives while providing context to physical assets affected by cyber attacks. The addressed problem is the inefficiency of personnel responding to cyber attacks due to the inability to tie them to physical assets. The solution is software that uses real-time data acquisition, management, and analysis of both cyber and biological data to detect anomalies and a metric that combines cyber and physical data to characterize the system comprehensively. The advantage of this solution over alternatives is its ability to distinguish between normal state, material faults, cyber attacks, and cyber-physical attacks through the consumption and integrated analysis of cyber and physical data sets.

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