Battelle Memorial Institute

William J Hofer

Thomas W Edgar

Draguna Vrabie

Kathleen E Nowak

Methods are described for protecting a cyber-physical system against a potential attacker of the system. The methods include a method of generating a plurality of examples for a training data set and training a system model using the training data set to generate a decoy configured to generate a synthetic output that mimics historical outputs generated by the system for a given historical system context. Also described is a method including receiving a system context of a cyber-physical system; receiving an inquiry into the system by a potential attacker; applying a system model to the system context and the inquiry; obtaining from the system model a synthetic output that mimics how a component of the system would respond to the inquiry given the system context; and providing the synthetic output to the potential attacker.

High-fidelity model-driven deception platform for cyber-physical systems

A system is described for protecting a cyber-physical system against a potential attacker of the cyber-physical system. The system includes at least one processor configured to: collect historical information about the cyber-physical system, and train, based on the historical information, a machine-learned model to predict future conditions of at least a portion of the cyber-physical system. Responsive to detecting an input signal to the cyber-physical system, the system is configured to output an alert to the cyber-physical system indicative of a potential attacker, and respond to the input signal by simulating, based on the future conditions predicted by the machine-learned model, functionality and communications of the at least a portion of the cyber-physical system.

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Richland, WA, United States of America

William J Hofer