Can Predictive Filters Detect Gradually Ramping False Data Injection Attacks Against PMUs?

Zhigang Chu, Andrea Pinceti, Reetam Sen Biswas, Oliver Kosut, Anamitra Pal, Lalitha Sankar

Abstract: Intelligently designed false data injection (FDI) attacks have been shown to be able to bypass the χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -test based bad data detector (BDD), resulting in physical consequences (such as line overloads) in the power system. In this paper, using synthetic PMU measurements and intelligently designed FDI attacks, 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.