Research Article

Countermeasures against collusive stealthy attacks in cyber-physical microgrids: A dynamic encoding approach

¹ School of Mechatronic Engineering and Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai, 200444, China
² School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

^* Corresponding authors (email: This email address is being protected from spambots. You need JavaScript enabled to view it. )

Received: 25 March 2025
Revised: 6 May 2025
Accepted: 8 July 2025

Abstract

Cyber-physical microgrids, as a representative industrial system, seamlessly integrate computation, communication, control, and physical devices, making it vulnerable to cyber-attacks that can trigger cascading failures and potentially lead to the collapse of the entire grid. This paper aims to develop an efficient detection framework for collusive stealthy attacks in cyber-physical microgrids. First, an ℒ_∞ unknown input observer (UIO) is deployed at the control center to monitor communication links between distributed generation units (DGUs). By treating interconnections and secondary control as unknown inputs, the observer gain is designed using only local information. Then, the vulnerability of the ℒ_∞ UIO-based monitoring unit is analyzed, and a collusive stealthy attack is devised to disrupt grid operations without alerting the ℒ_∞ UIO-based detection mechanism. To counteract the novel attack, a dynamic encoding mechanism is developed for the communication links between DGUs. This mechanism involves encoding control signals prior to transmission and subsequently decoding them at the control center. Furthermore, an in-depth analysis of the feasibility criteria for the encoding matrix has been conducted. Eventually, the efficacy of the proposed detection framework is validated through a series of simulation experiments.