Dynamic Reconfiguration for Resilient State Estimation Against Cyber Attacks

• Published in: IEEE transactions on emerging topics in computing Vol. 12; no. 2; pp. 559 - 571
• Main Authors: Callenes, Joseph, Poshtan, Majid
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bad data detection (BDD); Configurations; Critical infrastructure; critical infrastructure security; Cybersecurity; Distributed generation; distribution systems; Energy sources; false data injection; Flow paths; micro-grids cybersecurity; Microgrids; Power flow; Power system dynamics; Power systems; Reconfiguration; Security; Sensor systems; Sensors; State estimation
• ISSN: 2168-6750; 2168-6750
• DOI: 10.1109/TETC.2023.3266303

The increasing complexity and connectivity of critical infrastructures makes it increasingly likely that they will be subject to malicious attacks that compromise operation. Recent studies have shown that these systems are vulnerable to a wide range of cyber-attacks, including False Data Injection (FDI) attacks that bypasses conventional detection techniques. Conventional security monitoring, protection, and control tools are based primarily on passive defense strategies. In this paper, we propose an approach for active defense that improves system security and the detection rate of FDI attacks. The key insight for this approach is that emerging micro-grids can utilize distributed energy resources to dynamically reconfigure the system (e.g., power flow paths), across multiple acceptable configurations. Instead of using information from only a single static configuration to detect FDI attacks, our proposed approach uses dynamic reconfiguration to compare measured and estimated states under multiple configurations to accurately detect FDI attacks. We describe an implementation and supporting infrastructure for a secure reconfigurable microgrid. Dynamic reconfiguration also makes it more difficult for attackers to gain knowledge on the system's parameters, which increases the difficulty for attackers to construct hidden attack vectors. We evaluate our approach in the specific scenario of emerging micro-grids. We develop a novel technique for state estimation using multiple configurations and demonstrate that this approach significantly improves FDI detection accuracy.