Resilient Models for Attacks Detection in Cyber-Physical Systems
Project Details
Program
Electrical Engineering
Field of Study
Electrical and Computer Engineering/Computer Science
Division
Computer, Electrical and Mathematical Sciences and Engineering
Faculty Lab Link
Project Description
The goal of this project is developing methods to merge Machine Learning (ML) with physics-based models to create control algorithms can significantly enhance the resiliency of Cyber-Physical Systems (CPS). The approach will combine recent results in ML with control theory via constrained optimization to create novel systems and methods for protecting CPS from malicious cyber intruders via detection and prevention strategies. Specifically, the research project focuses on (1) refining the offline/online training and execution algorithms of ML models through physics-based constrained optimization, (2) developing secure estimation and control algorithms that are significantly more resilient to cyber-attacks than the state-of-the-art counterparts, and (3) improving the distributed resiliency for networked systems supporting multi-agent autonomous systems.
About the Researcher
Charalambos Konstantinou
Associate Professor, Electrical and Computer Engineering
Affiliations
Education Profile
- PhD, New York University, 2018
- MS, National Technical University of Athens, 2013
- BS, National Technical University of Athens, 2013
Research Interests
a€‹Professor Konstantinou's research interests are in secure, trustworthy, and resilient cyber-physical and embedded IoT systems. He is also interested in critical infrastructures security and resilience with special focus on smart grid technologies, renewable energy integration, and real-time simulation.Selected Publications
- I. Zografopoulos, J. Ospina, X. Liu, and C. Konstantinou, ""Cyber-Physical Energy Systems Security: Threat Modeling, Risk Assessment, Resources, Metrics, and Case Studies,"" in IEEE Access, vol. 9, pp. 29775-29818, 2021, doi: 10.1109/ACCESS.2021.3058403.
- C. Konstantinou, ""Cyber-Physical Systems Security Education Through Hands-on Lab Exercises,"" in IEEE Design & Test, vol. 37, no. 6, pp. 47-55, Dec. 2020, doi: 10.1109/MDAT.2020.3005365.
- O. M. Anubi and C. Konstantinou, ""Enhanced Resilient State Estimation Using Data-Driven Auxiliary Models,"" in IEEE Transactions on Industrial Informatics, vol. 16, no. 1, pp. 639-647, Jan. 2020, doi: 10.1109/TII.2019.2924246.
- C. Konstantinou, et al., ""GPS spoofing effect on phase angle monitoring and control in a real-time digital simulator-based hardware-in-the-loop environment"", IET Cyber-Physical Systems: Theory & Applications, 2017, 2, (4), p. 180-187, doi: 10.1049/iet-cps.2017.0033
- S. McLaughlin, C. Konstantinou, X. Wang, L. Davi, A. R. Sadeghi, M. Maniatakos, and R. Karri, ""The Cybersecurity Landscape in Industrial Control Systems,"" in Proceedings of the IEEE, vol. 104, no. 5, pp. 1039-1057, May 2016, doi: 10.1109/JPROC.2015.2512235.
Desired Project Deliverables
The goal of this internship is to analyze and improve existing data-driven and physics-based algorithms for attack detection in CPS. The student will be expected to learn about existing solutions, as well as the challenges and requirements to applying such techniques in their settings. With guidance of other team members, the student will then find new solutions for improving algorithmic resiliency in order to reduce cyber-risks related to the CPS operation. Candidates should be motivated to work on research-oriented problems with a team and develop new solutions. They should have a strong background in controls, in particular with regards to machine learning and power systems. They are also expected to be proficient in MATLAB/Simulink.
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Be part of the journey with VSRP
3-6 months
Internship period
100+
Research Projects
3.5/4
Cumulative GPA
310
Interns a Year