Adversarial Artificial Intelligence in Blind False Data Injection in Smart Grid AC State Estimation

Moshfeka Rahman, Jun Yan, Emmanuel Thepie Fapi

Abstract: Artificial intelligence (AI) plays an imperative role in next-generation critical infrastructures like the smart grid, whose power can be harnessed by not only operators, but also cyber adversaries. This article investigates a potential threat from adversarial AI in blind false data injection attacks (FDIA) targeting the ac state estimators in the smart grid. Assuming no access to the grid topology required in most FDIA, we propose an adversarial model based on artificial neural networks (ANNs) to infer grid topology from historical measurements. Following the topology inference, a substitute bad data detector (BDD) model is further proposed in the attack model to filter the false data before injection, reducing the risk of detection given potential bad data in normal operations. We also refine the common evaluation of FDI stealthiness by including the presence of bad data among normal and false data when assessing the detection performance. Simulations on the IEEE 30-bus system reveal that significant deviations can be inflicted stealthily by the proposed blind FDI attack. Detailed analyses of the stealthiness, impacts, and parameters are also presented to shed more light on the threats for further studies and effective countermeasures.