Publications Details

Detection of False Data Injection Attacks in Power System State Estimation Using Sensor Encoding

Trevizan, Rodrigo D.; Reno, Matthew J.

In this paper, we present a sensor encoding technique for the detection of stealthy false data injection attacks in static power system state estimation. This method implements low-cost verification of the integrity of measurement data, allowing for the detection of stealthy additive attack vectors. It is considered that these attacks are crafted by malicious actors with knowledge of the system models and capable of tampering with any number of measurements. The solution involves encoding all vulnerable measurements. The effectiveness of the method was demonstrated through a simulation where a stealthy attack on an encoded measurement vector generates large residuals that trigger a chi-squared anomaly detector (e.g. χ2). Following a defense in-depth approach, this method could be used with other security features such as communications encryption to provide an additional line of defense against cyberattacks.