Securing Automated, Adaptive Learning-Driven Cyber-Physical System Processes
Numerous DOE-relevant processes are becoming automated and adaptive, using machine learning techniques. Such processes include vehicle and traffic navigation guidance, intelligent transportation systems, adaptive control of grid-attached equipment, large scientific instruments.
This creates a vulnerability for a cyber attacker to sabotage processes through tainted training data or specially crafted inputs. Consequences might be tainted manufactured output, traffic collisions, power outages, or damage to scientific instruments or experiments. This project is developing secure machine learning methods that will enable safer operation of automated, adaptive, learning-driven “cyber-physical system” processes.
This project is supported by Berkeley Lab Contractor Supported Research funding.
Sean Peisert (PI; LBNL)
Daniel Arnold (Co-PI; LBNL)
Bashir Mohammed → DeepMind
Yize Chen (Postdoc) → Assistant Professor, University of Hong Kong
Publications resulting from this project:
Yize Chen, Yuanyuan Shi, Daniel Arnold, and Sean Peisert, ”SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation,” Proceedings of the 2022 IEEE Power Engineering Society (PES) General Meeting, Denver, CO. July 17-21 2022.
Yize Chen, Yuanyuan Shi, Daniel Arnold, and Sean Peisert, “SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation,” arXiv preprint arXiv:2111.15152, 30 Nov 2021.
Yize Chen, Daniel Arnold, Yuanyuan Shi, and Sean Peisert, “Understanding the Safety Requirements for Learning-based Power Systems Operations,” arXiv preprint arXiv:2110.04983, 11 Oct 2021
More information is available on other Berkeley Lab R&D projects focusing on cybersecurity in general, as well as specifically on cybersecurity for energy delivery systems.