A Fast, Decentralized Covariance Selection-Based Approach to Detect Cyber Attacks in Smart Grids

Recent studies have shown that an attacker can compromise some of the power grid measurements to mislead the conventional state estimators (SEs), since the manipulated measurements can pass the SE residue tests. Statistical structure learning-based approaches have been recently introduced as a powerful tool to detect some of the most complicated cyber attacks. However, the expensive computational complexity of the learning process limits the applicability of these approaches for real time cyber attack detection. This paper proposes a fast and decentralized approach for cyber attack detection based on a maximum likelihood (ML) estimation which exploits the near chordal sparsity of power grids to establish an efficient framework to solve the associated ML estimation problem. The proposed detection method is then decomposed to several local ML estimation problems; this would ensure privacy and reduce the complexity of the underlying problem. The simulation studies validate the efficiency of the proposed method in detecting truly complicated stealthy false data injection attacks.