The Data Science and Technology Department is an active participant in a number of projects in the arena of security for scientific, high-performance computing systems and high-bandwidth research and education networks. Research sponsors have typically included DOE’s ASCR program and NSF’s SaTC program, among others.
DST’s cybersecurity goals are to research, develop, evaluate, adapt, and integrate advanced security and privacy solutions that enable or improve scientific workflows that may otherwise not be possible due to real or perceived security restrictions that, using today’s solution, impose onerous usability and/or performance constraints, thereby hindering scientific progress.
LBNL has had a leadership role in security in scientific computing environments for many years, including the development of the Zeek (Bro) Network Security Monitor, the 100G performance enhancements of Zeek (Bro), and Zeek (Bro)’s commercial spin-off, Corelight, Inc., as well as leading several DOE-sponsored activities related to defining a cybersecurity research program within the DOE Office of Science. More recently, LBNL led the coordination of the “Cyber R&D” Enterprise Cyber Capability (ECC) of the DOE-wide Integrated Joint Cybersecurity Coordination Center (iJC3) — a sponsored R&D program involving ten DOE National Laboratories as performers.
ASCR Cybersecurity for Scientific Computing Workshop, June 2–3, 2015 [report]
DOE Cybersecurity R&D Challenges for Open Science: Developing a Roadmap and Vision, January 24–26, 2007 [news, report]
Into the Medical Science DMZ (Science Node) March 23, 2018
Berkeley Lab Researchers Contribute to Making Blockchains Even More Robust — January 30, 2018
Mind the gap: Speaking like a cybersecurity pro — Feb. 10, 2017
Building a CENIC Security Strategy — Jan. 11, 2017
Sean Peisert, Eli Dart, William K. Barnett, James Cuff, Robert L. Grossman, Edward Balas, Ari Berman, Anurag Shankar, and Brian Tierney, ”The Medical Science DMZ: An Network Design Pattern for Data-Intensive Medical Science”, Journal of the American Medical Informatics Association (JAMIA), 25,(3):267–274, March 2018.
Sean Peisert, “Security in High-Performance Computing Environments”, Communications of the ACM (CACM), 60(9):72-80, September 2017.
Sean Peisert, Von Welch, Andrew Adams, Michael Dopheide, Susan Sons, RuthAnne Bevier, Rich LeDuc, Pascal Meunier, Stephen Schwab, and Karen Stocks, Ilkay Altintas, James Cuff, Reagan Moore, and Warren Raquel, “Open Science Cyber Risk Profile,” February 2017.
Sean Whalen, Sean Peisert, Matt Bishop, “Multiclass Classification of Distributed Memory Parallel Computations,” Pattern Recognition Letters (PRL), 34(3):322-329, February 2013.
Sean Whalen, Sophie Engle, Sean Peisert, Matt Bishop, “Network-Theoretic Classification of Parallel Computation Patterns,” International Journal of High Performance Computing Applications (IJHPCA), 26(2):159-169, May 2012.
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 this center at Indiana University is Von Welch. LBNL’s role in this center is led by Sean Peisert.
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.
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.
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.
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.
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.
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.
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.
This project uses power data to monitor the use of computing systems, including supercomputers and large computing centers. It is led by Sean Peisert.
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.
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.
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.
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 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.
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.
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.
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.
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.
This project developed mathematical and statistical techniques to analyze the secure access and use of high-performance computer systems. It was funded by DOE ASCR and was originally led by David H. Bailey.
This project looked at defining means for understanding what data can be sanitized, and how. At LBNL, this project was led by Sean Peisert and was funded by the Institute for Information Infrastructure Protection (I3P).
This project is looking at establishing a rigorous, scientific model of forensic logging and analysis that is both efficient and effective at establishing the data that is necessary to record in order to understand past events. This work was led by Sean Peisert.
This project looked at defining, analyzing, and seeking methods of ameliorating the insider threat.