Ke Sun | Shanghai University | Research

Data Injection Attacks on the Smart Grid

• Outline: Using information-theoretic measures to quantify cybersecurity in cyber-physical systems, in particular data injection attacks in the smart grid. Random matrix theory tools were used to analyze the performance of the attack under imperfect system model knowledge conditions, which led to the derivation of bounds on the performance of the attacks for non-asymptotic scenarios and of closed-form expressions for the asymptotic case.

• Collaborator: Dr. Iñaki Esnaola, Dr. Samir M. Perlaza, Prof. Antonia M. Tulino, and Prof. H. Vincent Poor

• Selected Publications:

  • K. Sun, I. Esnaola, A. M. Tulino and H. V. Poor, ‘‘Asymptotic learning requirements for stealth attacks on linearized state estimation’’, IEEE Trans. Smart Grid, vol. 14, no. 4, pp. 3189 - 3200, Jul. 2023.

  • K. Sun, and Z. Li, ’’Sparse data injection attacks on smart grid: An information-theoretic approach’’, IEEE Sensors Journal, vol. 22, no. 14, pp. 14553 - 14562, Jul. 2022.

  • K. Sun, I. Esnaola, S. M. Perlaza, and H. V. Poor, ’’Stealth attacks on the smart grid," IEEE Trans. Smart Grid, vol. 11 , no. 2, pp. 1276-1285, Mar. 2020.

Information and Decision Making - IDEM

• Outline: This research undertakes fundamental research in applied mathematics at the intersection of information theory, game theory and machine learning. Specifically, a dynamic zero-sum game-theoretic model is proposed for the machine learning applications, such as adversarial learning, in which the attacker observes the behaviour of algorithms subjecting to distortion. Furthermore, the connection between the capacity of the observation channel and the corresponding game utility is established via information-theoretic notions. This research is supported by the Exploratory Action of INRIA. Link

• Collaborator: Dr. Samir M. Perlaza, Dr. Alain Jean-Marie

• Selected Publications:

  • K. Sun, S. M. Perlaza, and A. Jean-Marie, ’’2×2 zero-sum games with commitments and noisy observations", in Proc. IEEE Int. Symp. on Information Theory, Taipei, Taiwan, Jun. 2023, pp. 2254 - 2259.

Circular 4.0: Data Driven Intelligence for a Circular Economy

• Outline: This research aims to identify how data from products in use can inform intelligent decisions surrounding the implementation of circular economy strategies within manufacturing. Leveraging information-theoretic and data science tools, we (i) develop stochastic models for the data streams, (ii) provide quantitative measure for the amount information conveyed by data streams, and (iii) propose guidelines for optimal acquisition/sensing of the data stream based on the measure. Industrial collaborators include Rolls-Royce, Airbus, and Riversimple.

• Collaborator: Dr. Iñaki Esnaola, Prof. Ashutosh Tiwari, Prof. Fiona Charnley, and Dr. Okechukwu Okorie

• Selected Publications:

  • K. Sun, I. Esnaola, O. Okorie, F. Charnley, M. Moreno, A. Tiwari, ’’Data-driven modeling and monitoring of fuel cell performance’’, International Journal of Hydrogen Energy, vol. 46, no. 66, pp. 33206-33217, Sep. 2021.