Toward a Hardware/Software Co-Design Framework for Ensuring the Integrity of Exascale Scientific Data
Sean Peisert (PI)
LBNL-Affiliated Graduate Students:
Bogdan Copos (LBNL/UC Davis; Ph.D. 2017) → SRI International → Google
Prof. Hein Meling (LBNL/University of Stavanger)
Amir Teshome Wonjiga (LBNL/INRIA Rennes; Ph.D. 2019)
Reinhard Gentz (LBNL)
Anna Giannakou (LBNL)
Scientific data today is at risk due to how it is collected, stored, and analyzed in highly disparate computing systems. How can we make claims about the integrity of data as it traverses open, international networks and via instruments and systems with widely varying reliability and provenance? Numerous causes for integrity loss are possible, including bugs in existing computational pipelines, network events, user error, unintentional system effects or even intentional attack by outsiders (e.g., scientific competitors), insiders (e.g., disgruntled employees), or in the hardware/software supply chain, without any trace of the modification. Given these gaps and shortcomings in existing HPC solutions, how can we make claims about the integrity of the scientific data as it traverses those systems and networks?
We believe that in order to solve the problems described above that future HPC hardware and software solutions should be co-designed together with security and scientific computing integrity concepts designed and built into as much of the stack from the outset as possible. Given the risk of data loss due to software and hardware, this should take into account hardware elements, operating systems, compilers, application software, and the networking stack, all the way down to the way in which software developers write software and users interact with systems in a way that can affect scientific computing integrity. However, prior to laying out the research roadmap to design and construct such an architecture, we believe that several important aspects first need to be understood more clearly.
This project takes a broad look at several aspects of security and scientific integrity issues in HPC systems. Using several case studies as exemplars, and working closely with both domain scientists as well as facility staff, we propose to test and validate several initial concepts in existing scientific computing workflows at NERSC DOE HPC facility, and analyze those models better characterize integrity-related computational behavior.
Early work on this project focused on a range of activities, including identifying misuse of computing systens, leveraging blockchains for scientific computing. More recent work has focused on developing trustworthy scientific computing architectures.
For more on the current work, see Data Enclaves for Scientific Computing.
This project is supported by the US Department of Energy’s Office of Science’s Advanced Scientific Computing Research (ASCR) program.
Press regarding this project:
Into the Medical Science DMZ (Science Node) March 23, 2018
Berkeley Lab Researchers Contribute to Making Blockchains Even More Robust — January 30, 2018
ESnet’s Science DMZ Design Could Help Transfer, Protect Medical Research Data (Science Node) — October 16, 2017
Publications resulting from this project:
Ross Gegan, Christina Mao, Dipak Ghosal, Matt Bishop, and Sean Peisert, “Anomaly Detection for Science DMZs Using System Performance Data,” Proceedings of the 2020 IEEE International Conference on Computing, Networking and Communications (ICNC 2020), Big Island, HI, February 17–20, 2020.
Amir Teshome Wonjiga, Louis Rilling, Christine Morin, and Sean Peisert, “Blockchain as a Trusted Component in Cloud SLA Verification,” Proceedings of the International Workshop on Cloud, IoT and Fog Security (CIFS), co-located with the 12th IEEE/ACM International Conference on Utility and Cloud Computing (UCC), Auckland, New Zealand, December 2–5, 2019.
Amir Teshome Wonjiga, User-Centric Security Monitoring in Cloud Environments. PhD dissertation, Inria Rennes – Bretagne Atlantique, May 2019. (Dissertation Advisors: Christine Morin and Louis Rilling)
Anna Giannakou, Daniel Gunter, and Sean Peisert, “Flowzilla: A Methodology for Detecting Data Transfer Anomalies in Research Networks,” Proceedings of the 5th Innovate the Network for Data-Intensive Science (INDIS) Workshop, Dallas, TX, November 11, 2018.
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.
Bogdan Copos, Modeling Systems Using Side Channel Information. PhD dissertation, University of California, Davis, 2017. (Dissertation Advisor: Sean Peisert)
Sean Peisert, William K. Barnett, Eli Dart, James Cuff, Robert L. Grossman, Edward Balas, Ari Berman, Anurag Shankar, and Brian Tierney, “The Medical Science DMZ,” Journal of the American Medical Informatics Association (JAMIA), 23(6), Nov. 1, 2016.
Software resulting from this project:
Sean Peisert, “Keynote: Cybersecurity for HPC Systems: State of the Art and Looking to the Future,” High-Performance Computing Security Workshop, National Institute of Standards and Technology (NIST), Gaithersburg, MD, March 28, 2018,
Sean Peisert, “Security in High Performance Computing Environments,” Computing Sciences/NERSC Security Seminar, Lawrence Berkeley National Laboratory, October 5, 2017,
Sean Peisert, “Security and Privacy in Data-Intensive, High-Performance Computing Contexts,” Berkeley Institute for Data Science (BIDS), University of California, Berkeley, October 2, 2017,
Lee Beausoleil (NSA), David Lombard (Intel), Angelos Keromytis (DARPA), Sean Peisert (LBNL), “Panel: HPC Monitoring,” NSCI: High-Performance Computing Security Workshop, National Institute of Standards and Technology (NIST), Gaithersburg, MD, September 30, 2016,
Sean Peisert, Security Expert on Why HPC Matters - Cybersecurity for HPC Systems: Challenges and Opportunities, NSCI: High-Performance Computing Security Workshop, National Institute of Standards and Technology (NIST), Gaithersburg, MD, September 29, 2016,
- Data Enclaves for Scientific Computing
- A Mathematical and Data-Driven Approach to Intrusion Detection for High-Performance Computing
- Detecting Distributed Denial of Service Attacks on Wide-Area Networks
- Inferring Computing Activity Using Physical Sensors
- Privacy-Preserving Data Analysis for Scientific Discovery