Work is ongoing in identifying cyber threats, vulnerabilities and locating hacker penetration in electric utility and other operational technology (OT) networks. However, existing technologies including Intrusion Detection Systems, Intrusion Prevention Systems, Security Information and Event Management and Security Orchestration, Automation and Response can’t predict the future movement of a cyber intrusion that has successfully breached the OT network.
Under U.S. Air Force and Department of Energy (DOE) contracts, the Global Center for Advanced Studies and its subcontractors, Lockheed Martin, Applied Control Solutions, Carnegie Mellon University and Georgia Tech, have developed a prototype modeling approach to forecast a cyber threat’s future maneuvers in compromised OT networks – the Cyber Attack Forecasting Systems (CAFS). The approach is based on techniques used by the DoD for ballistic missile attack warning and assessment, including probabilistic multi-model filters and multi-hypothesis method within a Bayesian framework.
Figure 1
This proven technology has been leveraged to track and forecast future cyber threat attack vectors for effective defense of organizational high value assets and to neutralize those threats. The framework models different classes of threat actors and their behaviors/capabilities in great detail. The result is the addition of a new dimension to cyber defense of predicting the potential next move of the attack vector once inside an OT network (Figure 1). This is particularly important for ICS OT cybersecurity because so many attacks on those systems have originated in business networks and subsequently pivoted into the OT networks.
This technology became critical as the DOE-sponsored literature search found that the most recent and comprehensive work on cyberattack forecasting was by Iranian researchers.
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