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Smart, Secure and Resilient Energy Systems

 

Cyber-Physical Systems Security through Robust Adaptive Possibilitistic Algorithms: a Cross Layered Framework

Project Sponsor: National Science Foundation, Award#1809739.

Personnel:
Faculty
Energy: Arturo Bretas
Networks: Janise McNair
Machine Learning: Alina Zare

Current Graduate Students
Energy:   Michel Caraballo-Gomez, Grenoble INP UGA
Communications and Networking:   Sharon Boamah
Machine Learning:   Priya Mittal

Undergraduate Students
Jonathan Box, Victor Faillace
NSF Research Experiences for Undergraduates (REU): Secure, Accessible, and Sustainable Transportation (SAST)

Project Goal:

The goal of this project is to develop a cross-layer cyber-physical security framework for the smart grid. The proposed research will improve the quality of real-time monitoring of the smart grid through anomaly analysis. This will lead to more reliable data for control, situation awareness to first responders and other improved applications to smart grids. The proposed research will improve the resilience of smart grids to cyber-attacks in meters, parameters, topology and communication infrastructure and large physical disturbances by developing new techniques for distributed control of large complex systems that guarantees secure and reliable performance. The project will foster education through enhancement to curriculum by building bridges among communications, machine learning, power and control systems. The PIs plan to teach short courses on smart grid security at conferences. In addition, they plan to engage under-represented minority students in their project.

 
Publications
  • Submitted
    1. TBA
  • To Appear
    1. TBA
  • Published
    1. S. Boamah, R. Mathieu, V. Faillace, D. Agnew, J. McNair, & A. Bretas.  Data Fusion-based Hybrid Framework for Cyber-physical Security of the Smart Grid. In 2024 IEEE Resilience Week (RWS) (pp. 1-8), December 2024.
    2. Agnew D, Boamah S, Bretas A, and McNair, J. Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey. Smart Cities. 2024; 7(4):2131-2181. 
    3. R. Mathieu, S. Boamah, A. Cooper, D. Agnew, J. McNair and A. Bretas, “Communication Network Layer State Estimation Measurement Model for a Cyber-Secure Smart Grid,” 2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2024, pp. 1-5.
    4. D. Agnew, S. Boamah, R. Mathieu, A. Cooper, J. McNair, and A. Bretas,  “Distributed Software-Defined Network Architecture for Smart Grid Resilience to Denial-of-Service Attacks,” 2023 IEEE PES General Meeting, July 2023. Selected for presentation in the Best Papers Session.
    5. A. Starke, K. Nagaraj, C. Ruben, N. Aljohani, S. Zou, A. Bretas, J. McNair, A. Zare, “Cross-Layered Distributed Data-Driven Framework for Enhanced Smart Grid Cyber-Physical Security,” IET Smart Grid, 5( 6), 398– 416 (2022). 
      **This article has been selected as the featured article in this issue.
    6. D. Agnew, N. Aljohani, R. Mathieu, S. Boamah, K. Nagaraj, J. McNair, and A. Bretas, “Implementation Aspects of Smart Grids Cyber-Security Cross-Layered Framework for Critical Infrastructure Operation”  Applied Sciences Special Issue on Electrification of Smart Cities, 2022, volume 12, number 14, pp. 1-19.
    7. N. Aljohani, D. Agnew Jr., K. Nagaraj, S.A. Boamah, R. Mathieu, A.S. Bretas, J. McNair, A. Zare, “Cross-Layered Cyber-Physical Power System State Estimation towards a Secure Grid Operation,” IEEE Power & Energy Society (PES) General Meeting, July 2022.
    8. K. Nagaraj, N. Aljohani, S. Zou, T. Zou, A. Bretas, J. McNair, A. Zare, “Smart FDI Attack Design and Detection with Data Transmutation Framework for Smart Grids,” IEEE PES General Meeting, July 2021.
    9. T. Zou, N. Aljohani, K. Nagaraj, S. Zou, C. Ruben, A. Bretas, A. Zare, and J. McNair, A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach Applied Sciences, vol. 11, no. 17, p. 8074-8089, Aug. 2021 [Online]. Available: http://dx.doi.org/10.3390/app11178074
    10. K. Nagaraj, A. Starke and J. McNair, GLASS: A Graph Learning Approach for Software Defined Network Based Smart Grid DDoS Security, IEEE International Conference on Communications (ICC), 2021, pp. 1-6, doi: 10.1109/ICC42927.2021.9500999.
    11. K. Nagaraj, N. Aljohani, S. Zou, T. Zou, A. Bretas, J. McNair, and Alina Zare . State Estimator and Machine Learning Analysis of Residual Differences to Detect and Identify FDI and Parameter Errors in Smart Grids, in Proceedings of the 52nd North American Power Symposium (NAPS) (pp. 1-6). IEEE. April 2021.
    12. Nagaraj, K., Zou, S., Ruben, C., Dhulipala, S., Starke, A., Bretas, A., McNair, J., Zare, A., “Ensemble CorrDet with Adaptive Statistics for Bad Data Detection,” accepted for publication in IET Smart Grid Journal, available online in June 2020. DOI: 10.1049/iet-stg.2020.0029
    13. Ruben, C., Dhulipala, S., Nagaraj, K., Zhou, S., Starke, A., Bretas, A., Zare, A., and McNair, J., “Hybrid data driven physics model-based framework for enhanced cyber-physical smart grid security,” accepted for publication in IET Smart Grid Journal, available online in December 2019. DOI:10.1049/iet-stg.2019.0272
    14. R.D. Trevizan, C. Ruben, K. Nagaraj, Layiwola L. Ibukun, A.C. Starke, A. Bretas, J. McNair, A. Zare. Data-driven Physics-based Solution for False Data Injection Diagnosis in Smart Grids. IEEE Power & Energy Society General Meeting (PESGM), August 4-8 2019, Atlanta, GA, USA. DOI: http://doi.org/10.1109/PESGM40551.2019.8974027
    15. A. Starke, J. McNair, R. Trevizan, A. Bretas, J. Peeples and A. Zare, “Toward Resilient Smart Grid Communications Using Distributed SDN with ML-Based Anomaly Detection” In: Chowdhury K., Di Felice M., Matta I., Sheng B. (eds) Wired/Wireless Internet Communications. WWIC 2018. Lecture Notes in Computer Science, vol 10866. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-02931-9_7