Co-Simulation Framework For Network Attack Generation and Monitoring
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24 POWER TRANSMISSION AND DISTRIBUTION↗
Engineering topics
Hofer, William J.
Publications and source records attributed to Hofer, William J..
High-fidelity model-driven deception platform for cyber-physical systems
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.
Edgar, Thomas W.↗
Design and Development of a High Fidelity Cyber-Physical Testbed
In order to ensure that future critical infrastructure systems are resilient to various types of such advanced and persistent threats, it is important to develop and integrate tailored solutions that holistically address cyber-attack detection and mitigation in a timely manner such that adverse system impacts that impact a large population are avoided. Further, it is essential to create environments that allow control, protection and communication to exist within a realistic environment to analyze the effects of adverse conditions and system operating modes. This project aims to establish a high-fidelity testbed environment for modeling and simulating a single microgrid all the way up to a network of microgrids along with baseline controls, protection, and associated cyber communication. This is an important activity because accurately modeling and simulating the various power-electronics-based DERs and loads in a microgrid is critical to adequately capturing their behaviors over a wide range of off-normal conditions, as well as to evaluate the resilience of the system using the developed controls. The work presented in this report focuses on the process of building this high-fidelity testbed and the associated experimentation it enables. The model enables the creation of high-fidelity use cases and associated datasets that have been used extensively within the initiative to study resilience and support novel control development and prototyping. The work heavily leverages existing capability that is part of the high-fidelity cyber-physical system experimentation lab to create a power hardware-in-the-loop setup. The report also details the creation of an automated model building platform that can enable high-fidelity real-time models to be built without much effort allowing existing low-fidelity models to be analyzed in higher fidelity. Lastly, the report also discusses efforts center around scaling to large complex power system models to make the experimentation more effective.
97 MATHEMATICS AND COMPUTING↗
Increased Interpretability for Model-Driven Deception: MARS LDRD Project
Machine learning has been proposed as a solution to several cybersecurity solutions and one of the most promising applications is for digital twins for intrusion detection and driving deceptive defense. However, machine learning techniques often result in a black-box function that is difficult for end users to interpret which for deception limits their ability to effectively define decoys. In this report, an approach to validate the equations learned are accurate is provided and demonstrated. Following, begins the process of addressing this issue for a model-driven deception technology that produces equations representing the physical process controlled by operation technology devices. This research was performed by applying subject matter expert context to machine learned models.
97 MATHEMATICS AND COMPUTING↗