Cybersecurity for Energy Delivery Systems Projects

Berkeley Lab Computing Sciences Research 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’s Cybersecurity for Energy Delivery Systems (CEDS) R&D program. These projects include collaborations with academic, vendor, and utility partners.

Berkeley Lab’s work in security for power grid control systems emphasizes both its historical role in developing, deploying and testing the Zeek (Bro) Network Security Monitor, as well as novel ideas that leverage and integrate 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.

Recent highlights of Berkeley Lab Computing Sciences’ cybersecurity R&D activities in cybersecurity for energy delivery systems include:

Development and application of differential privacy to power grid and vehicle mobility data and applications ⇒ The DOE Office of Cybersecurity, Energy Security, and Emergency Response (CESER) is seeking to deploy the former operationally and the latter has already enabled mobility research otherwise not possible due to data sharing restrictions.
Developed the first practical approaches to integrate physics of operational technology in the power grid with intrusion detection to ensure their secure operation. ⇒ Now broadly used in applied R&D efforts globally, and appear in DOE funding solicitations and Congressional budget appropriations.
Developing a technique to enable automated response to cyberattacks against rooftop solar inverters.

Some recent news:

Berkeley Lab Leading the Way with New Cybersecurity Projects — Nov. 6, 2023

Older News

Key Representative Publications:

Robert Currie, Sean Peisert, Anna Scaglione, Aram Shumavon, and Nikhil Ravi, “Data Privacy for the Grid: Toward a Data Privacy Standard for Inverter-Based and Distributed Energy Resources,” IEEE Power & Energy Magazine, 21(5), pp. 58-57, Sept.-Oct 2023.

Raksha Ramakrishna, Anna Scaglione, Tong Wu, Nikhil Ravi, and Sean Peisert, “Differential Privacy for Class-based Data: A Practical Gaussian Mechanism,” IEEE Transactions on Information Forensics and Security, 23 June 2023.

Nikhil Ravi, Anna Scaglione, Sachin Kadam, Reinhard Gentz, Sean Peisert, Brent Lunghino, Emmanuel Levijarvi, and Aram Shumavon, “Differentially Private K-means Clustering Applied to Meter Data Analysis and Synthesis,” IEEE Transactions on Smart Grid, June 17, 2022.

Ciaran Roberts Sy-Toan Ngo, Alexandre Milesi, Sean Peisert, Daniel Arnold, Shammya Saha, Anna Scaglione, Nathan Johnson, Anton Kocheturov, Dmitriy Fradkin, “Deep Reinforcement Learning for DER Cyber-Attack Mitigation,” Proceedings of the IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), November 11–13, 2020.

Ciaran Roberts, Anna Scaglione, Mahdi Jamei, Reinhard Gentz, Sean Peisert, Emma M. Stewart, Chuck McParland, Alex McEachern, and Daniel Arnold, “Learning Behavior of Distribution System Discrete Control Devices for Cyber-Physical Security,” IEEE Transactions on Smart Grid, accepted 31 July, 2019. [DOI]

Mahdi Jamei, Anna Scaglione, Ciaran Roberts, Emma Stewart, Sean Peisert, Chuck McParland, and Alex McEachern, “Anomaly Detection Using μPMU Measurements in Distribution Grids,” IEEE Transactions on Power Systems, 33(4), pp. 3611–3623, October 25, 2017. [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," 20(5), pp. 18-27, Sept./Oct. 2016. [DOI]

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]

Software

A portion of the software developed through this project can be downloaded via Github.

Projects

Mitigation via Analytics for Inverter-Grid Cybersecurity (MAGIC)

Project MAGIC will develop artificial intelligence and machine learning algorithms to detect and mitigate cyber attacks on aggregations of Distributed Energy Resources (DER). The developed algorithms will be demonstrated in hardware-in-the-loop testing and integrated into an open source simulation tool. It is funded by DOE CESER’s RMT program and is led by Daniel Arnold.

Privacy-Preserving, Collective Cyberattack Defense of DERs

This project aims to develop, apply, and test a technique for enabling collective defense of distribution grids with significant penetration of distributed energy resources (DER) and responsive loads, by leveraging a privacy-preserving method of data sharing without exposing raw data that might contain personally identifiable information (PII) or that might otherwise be considered national security information that could be leveraged by adversaries to more effectively compromise and potentially destabilize portions of the electric grid. It is funded by DOE CESER’s RMT program and is led by Sean Peisert.

Trusted CI, the National Science Foundation Cybersecurity of Excellence

The mission of Trusted CI is to improve the cybersecurity of NSF computational science and engineering projects, while allowing those projects to focus on their science endeavors. The PI of Trusted CI at University of Illinois, Urbana-Champaign is Jim Basney. LBNL’s role in Trusted CI is led by Sean Peisert.

Using Fuzz Testing to Detect Software Tampering

This project aims verify that software operating on arms control monitoring equipment is within agreed parameters. It is funded by the National Nuclear Security Administration Office of Defense Nuclear Nonproliferation Research and Development and is led by Sean Peisert.

Data Enclaves for Scientific Computing

This project will develop secure computation architectures to ensure trustworthiness of scientific data while addressing the gaps left by existing solutions for scientific workflows to address the specific power, performance, and usability, and needs from the edge to the HPC center. It is led by Sean Peisert, Venkatesh Akella, and Jason Lowe-Power.

Privacy-Preserving Data Analysis for Scientific Discovery

This project aims to produce methods, processes, and architectures applicable to a variety of scientific computing domains that enables querying, machine learning, and analysis of data while protecting against releasing sensitive information beyond pre-defined bounds. It is supported by LBNL CSR funds and is led by Sean Peisert.

Securing Automated, Adaptive Learning-Driven Cyber-Physical System Processes

This project is developing secure machine learning methods that will enable safer operation of automated, adaptive, learning-driven cyber-physical system processes. It is supported by LBNL LDRD funds and is led by Sean Peisert.

Provable Anonymization of Grid Data for Cyberattack Detection

This project aims to develop techniques for enabling data analysis for the purposes of detecting and/or investigating cyberattacks against energy delivery systems while also preserving aspects of key confidentiality elements within the underlying raw data being analyzed. The result will be a complete solution for anonymization of data collected from OT and IT networks pertaining to energy grid cyberattack detection that has been tested for its ability to retain privacy properties and still enable attack detection. It is funded by DOE CESER’s CEDS program and is led by Sean Peisert.

Supervisory Parameter Adjustment for Distribution Energy Storage (SPADES)

This project is developing the methodology and tools allowing Electric Storage Systems (ESS) to automatically reconfigure themselves to counteract cyberattacks, both directly against the ESS control systems and indirectly through the electric grid. It is funded by DOE CESER’s CEDS program and is led by Daniel Arnold.

Securing Solar for the Grid (S2G)

This project aims to develop an understanding of security and performance requirements for the use of AI high solar / IBR / DER penetration scenarios, and also to develop an understanding of understanding power grid data confidentiality and privacy requirements. It is funded by DOE’s SETO office and is co-led by Sean Peisert and Daniel Arnold.

AOSCSWAP: Study of Academic, Open Source, and COTS Software Assurance Products

In this project, LBNL will help inform DHS S&T regarding the state of the art in software assurance tools and capabilities. It is funded by DHS S&T and is led by Sean Peisert.

Cybersecurity via Inverter-Grid Automatic Reconfiguration (CIGAR)

This project performed 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 was funded by DOE’s CEDS program and was co-led by Sean Peisert and Daniel Arnold.

Byzantine Security — Multi-layered Intrusion Tolerant Byzantine Architecture for Bulk Power System Protective Relays

This project aims to explore applications of a Byzantine Fault Tolerant (BFT) architecture in combination with ML/AI methods to ensure that the bulk power system, including protective relays in the transmission grid, and associated substation and control center systems, can perform intrusion tolerant operations. It is funded by the DOE Grid Modernization Initiative. The LBNL portion of this effort is led by Sean Peisert.

Synthetic Biology Automation

In this project, LBNL Computing Sciences Research supported the automation of synthetic biology research pipelines supporting the design-build-test-learn (DBTL) cycle, including ingest and analysis of liquid chromatography mass spectrometry and feedstocks-to-fuels pipelines.

Medical Science DMZ

We have defined a Medical Science DMZ as a method that allows data flows at scale while simultaneously addressing the HIPAA Security Rule and related regulations governing biomedical data and appropriately managing risk.

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.

Integrated Multi Scale Machine Learning for the Power Grid

The goal of this project is to create advanced, distributed data analytics capability to provide visibility and controllability to distribution grid operators. It is funded by the DOE Grid Modernization Initiative. The LBNL portion of this effort is led by Sean Peisert.

Detecting Distributed Denial of Service Attacks on Wide-Area Networks

This project develops techniques for detecting DDoS attacks and disambiguating them from large-scale science flows. It is funded by the DOE iJC3 Cyber R&D program and is led by Sean Peisert.

Toward a Hardware/Software Co-Design Framework for Ensuring the Integrity of Exascale Scientific Data

This project takes a broad look at several aspects of security and scientific integrity issues in HPC systems. It is funded by DOE ASCR and is led by Sean Peisert.

Power Grid Threat Detection and Response with Data Analytics

The goal of this project is to develop technologies and methodologies to protect the nation’s power grid from advanced cyber and all-hazard threats. This will be done through the collection of disparate data and the use of advanced analytics to detect threats and response to them. It is funded by DOE OE’s CEDS program via the Grid Modernization Initiative and is co-led by Sean Peisert.

Inferring Computing Activity Using Physical Sensors

This project uses power data to monitor the use of computing systems, including supercomputers and large computing centers. It is led by Sean Peisert.

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 power distribution systems. It is funded by DOE OE’s CEDS program and is led by Sean Peisert.

Bedside to the Cloud and Back

This project is developing a system-based workflow to securely acquire wireless data from mechanical ventilators in critical care environments, and leverage scalable web-based analytic platforms to advance data analytics and visualization of issues surrounding patients with respiratory failure.

Host and Network Resilience

This project focused on mapping and analyzing the qualities of resilient networks by investigating components of redundancy, diversity, quality of service, etc… The project’s goal is to be able to quantify and compare the resilience of networks in a scientifically meaningful way. This project was led at LBNL by Sean Peisert.

Symbiosis in Byzantine Fault Tolerance and Intrusion Detection

This project was funded by NSF’s SaTC program, and was co-led by Sean Peisert. The theme of this effort was to integrate Byzantine fault tolerance (BFT) into intrusion detection systems (IDS), at both the fundamental and system levels, thereby improving both BFT and IDS. potential to improve BFT.

NetSage - an open privacy-aware network measurement, analysis, and visualization service

NetSage is a network measurement, analysis and visualization service funded by the National Science Foundation and is designed to address the needs of today’s international networks. This project is co-led by Sean Peisert at LBNL.

Cyber Security of Power Distribution Systems by Detecting Differences Between Real-time Micro-Synchrophasor Measurements and Cyber-Reported SCADA

This project used micro-PMU measurements and SCADA commands to develop a system to detect cyberattacks against the power distribution grid. It was funded by DOE OE’s CEDS program and was led by Sean Peisert.

LBNL Power Data

This distribution level phasor measurement data can be used to understand ways to enables advanced diagnostic, monitoring and control methodologies in distribution systems.

NNSA Cyber Sciences Lab (CSL)

Using seed funding from the NNSA CIO, this consortium of eight DOE laboratories worked to form an enduring, national computer security research laboratory to address cybersecurity threats. LBNL’s effort was led by Deb Agarwal and Sean Peisert.

The Hive Mind: Applying a Distributed Security Sensor Network to GENI.

This project sought to define and prototype a security layer using a method of intrusion detection based on mobile agents and swarm intelligence. The project was funded by NSF’s CISE Directorate, and was led by Sean Peisert.

Application of Cyber Security Techniques in the Protection of Efficient Cyber-Physical Energy Generation Systems

The goal of this project was to design and implement a measurement network, which can detect and report the resultant impact of cyber security attacks on the distribution system network. It was funded by DOE OE’s CEDS program and was co-led by Chuck McParland and Sean Peisert.