Inferring Computing Activity Using Physical Sensors
Sean Peisert (PI)
This project involves using power data for monitoring use of computing systems, including supercomputers and large computing centers. By using power data, as opposed to data provided by the computing environment itself, the technology collects the data non-invasively. More information is available at the LBNL Innovation and Partnerships Office.
Publications resulting from this project:
Bogdan Copos and Sean Peisert, “Catch Me If You Can: Using Power Analysis to Identify HPC Activity,” arXiv preprint arXiv:2005.03135, 2020.
Melissa Stockman, Dipankar Dwivedi, Reinhard Gentz, Sean Peisert, “Detecting Programmable Logic Controller Code Using Machine Learning” International Journal of Critical Infrastructure Protection, accepted July 3, 2019. [DOI]
Bogdan Copos, Sean Peisert (advisor), Modeling Systems Using Side Channel Information. PhD dissertation, University of California, Davis, 2017.
Software resulting from this project:
- Toward a Hardware/Software Co-Design Framework for Ensuring the Integrity of Exascale Scientific Data
- Cyber Security of Power Distribution Systems by Detecting Differences Between Real-time Micro-Synchrophasor Measurements and Cyber-Reported SCADA
- A Mathematical and Data-Driven Approach to Intrusion Detection for High-Performance Computing
- Data Enclaves for Scientific Computing
- Securing Solar for the Grid (S2G)