Cybersecurity for Energy Delivery Systems Research and Development
The Berkeley Lab Data Science and Technology Department is an active participant in a number of projects in the arena of cybersecurity for energy delivery systems. Recently, this work has been funded largely via DOE OE's Cybersecurity for Energy Delivery systems program. These projects include collaborations with academic, vendor, and utility partners. LBNL's work in this space emphasizes both its historical role in developing, deploying and testing the Bro Network Security Monitor, as well as novel research ideas that leverage physics — physical limitations, physical sensor output, and insight into commands sent to control systems — to help monitor and protect networked energy system devices under control.
Learn more at the LBNL Cybersecurity for Energy Delivery Systems R&D Web Site.
A partial listing of current and recent R&D projects relating to cybersecurity for energy delivery systems, including lists of partners, publications, and software developed is as follows:
- Adaptive Control of Electric Grid Components for Cyber Resiliency. This project is performing R&D to enable distribution grids to adapt to resist a cyber-attack by (1) developing adaptive control algorithms for DER, voltage regulation, and protection systems; (2) analyze new attack scenarios and develop associated defensive strategies. It is funded by DOE OE's CEDS program and is co-led by Sean Peisert and Daniel Arnold. See CEDS CIGAR project website.
- UC-Lab Center for Electricity Distribution Cybersecurity. This project will bring together a multi-disciplinary UC-Lab team of cybersecurity and electricity infrastructure experts to investigate, through both cyber and physical modeling and physics-aware cybersecurity analysis, the impact and significance of cyberattacks on electricity distribution infrastructure. It is funded by the UC-Lab Fees Research Program. The overall project is led by Hamed Mohsenian-Rad; the LBNL portion is led by Sean Peisert. See UC-Lab Center project website.
- Threat Detection and Response with Data Analytics. This project is developing technologies and methodologies to protect the grid from advanced cyber and all-hazard threats through the collection of disparate data and the employment of advanced analytics for threat detection and response. The project is funded by DOE OE's CEDS program as part of the DOE Grid Modernization Initiative. The project is led by LLNL, co-led by Sean Peisert at LBNL, and also includes partnerships with PNNL and SNL. Utility partners include the Electric Power Board (EPB), National Rural Electric Cooperative Association (NRECA) See CEDS Threat Detection and Response with Data Analytics project website.
- Integrated Multi Scale Machine Learning. This project’s overarching goal is to create advanced, distributed data analytics capability within the DOE GM Consortium, to provide visibility, and controllability to distribution grid and building operators. The project is funded by DOE EERE and DOE OE as part of the DOE Grid Modernization Initiative. The project is led by LLNL. Sean Peisert is the lead at LBNL. It als includes partnerships with LANL, NREL, ORNL, and SNL Utility partners include the Riverside Public Utilities and Florida Power and Light. Vendor partners include National Instruments, PingThings, and Power Standards Laboratory. See Integrated Multi Scale Machine Learning project website.
Several recent projects include the following:
- An Automated, Disruption Tolerant Key Management System for the Power Grid. This project is designing and developing a key management system to meet the unique requirements of electrical distribution systems (EDSs). It is funded by DOE OE's CEDS program, is a partnership with PNNL, and is led at LBNL by Sean Peisert. See Power Grid Key Management project website.
- Cyber Security of Power Distribution Systems by Detecting Differences Between Real-time Micro-Synchrophasor Measurements and Cyber-Reported SCADA.This project is using micro-PMU measurements and SCADA commands to develop a system to detect cyberattacks against the power distribution grid. It is funded by DOE OE's CEDS program and is led by Sean Peisert. See µPMU Cyber Security project website.
- Inferring Computing Activity Using Physical Sensors. This project is using power data to identify computational operations, particularly in high-performance and cloud computing environments. This project is led by Sean Peisert at LBNL. See project website for inferring computing activity with power data.
- Application of Cyber Security Techniques in the Protection of Efficient Cyber-Physical Energy Generation Systems. This project was funded by DOE OE's CEDS program and was co-led by Chuck McParland and Sean Peisert. Specifically, we designed and developed a security monitoring and analysis framework for control systems. The goal was to integrate the monitoring and analysis of network traffic and serial communication with an understanding of physical device constraints within a single intrusion detection system (IDS) to enhance resilience of cyber physical systems. See CEDS CPS security project website.
Key Representative Publications:
Chuck McParland, Sean Peisert, and Anna Scaglione, "Monitoring Security of Networked Control Systems: It's the Physics," IEEE Security and Privacy,12(6), November/December 2014. [BibTeX] [DOI]
Mahdi Jamei, Emma Stewart, Sean Peisert, Anna Scaglione, Chuck McParland, Ciaran Roberts, and Alex McEachern, "Micro Synchrophasor-Based Intrusion Detection in Automated Distribution Systems: Towards Critical Infrastructure Security," IEEE Internet Computing," Sept./Oct. 2016. [CDL]
A portion of the software developed through this project can be downloaded via Github.