| Title | Detection of False Data Injection Attacks in Unobservable Power Systems by Laplacian Regularization |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Dabush, Lital, Routtenberg, Tirza |
| Conference Name | 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM) |
| Keywords | array signal processing, attack detection, bad-data detection, composability, Conferences, Cyber-physical systems, Electric variables measurement, False Data Detection, false data injection (FDI) attacks, Graph signal processing (GSP), Human Behavior, Laplace equations, Loss measurement, power system state estimation (PSSE), Power systems, pubcrawl, resilience, Resiliency |
| Abstract | The modern electrical grid is a complex cyber-physical system, and thus is vulnerable to measurement losses and attacks. In this paper, we consider the problem of detecting false data injection (FDI) attacks and bad data in unobservable power systems. Classical bad-data detection methods usually assume observable systems and cannot detect stealth FDI attacks. We use the smoothness property of the system states (voltages) w.r.t. the admittance matrix, which is also the Laplacian of the graph representation of the grid. First, we present the Laplacian-based regularized state estimator, which does not require full observability of the network. Then, we derive the Laplacian-regularized generalized likelihood ratio test (LR-GLRT). We show that the LR-GLRT has a component of a soft high-pass graph filter applied to the state estimator. Numerical results on the IEEE 118-bus system demonstrate that the LR-GLRT outperforms other detection approaches and is robust to missing data. |
| DOI | 10.1109/SAM53842.2022.9827810 |
| Citation Key | dabush_detection_2022 |
Detection of False Data Injection Attacks in Unobservable Power Systems by Laplacian Regularization