Cyber-Physical Systems Security through Robust Adaptive Possibilitistic Algorithms: a Cross Layered Framework
Project Sponsor: National Science Foundation, Award#1809739.
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.
- Starke, A., 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,” submitted March 2020.
- To Appear
- 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.
- 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
- 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.
- 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.
- 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
- 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
- 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
- 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