Can Predictive Filters Detect Gradually Ramping False Data Injection Attacks Against PMUs?
For power system operators, this work identifies a vulnerability in predictive filter-based detection methods against stealthy FDI attacks.
The paper shows that predictive filters can detect sudden false data injection attacks against PMUs, but an attacker can gradually ramp up the attack magnitude to avoid detection while still causing damage.
Intelligently designed false data injection (FDI) attacks have been shown to be able to bypass the $χ^2$-test based bad data detector (BDD), resulting in physical consequences (such as line overloads) in the power system. In this paper, it is shown that if an attack is suddenly injected into the system, a predictive filter with sufficient accuracy is able to detect it. However, an attacker can gradually increase the magnitude of the attack to avoid detection, and still cause damage to the system.