Dynamic Reconfiguration for Resilient State Estimation Against Cyber Attacks

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.