Detection of false data injection attacks leading to line congestions using Neural networks

The connection between smart grids and buildings in smart cities can align energy supply and demand more efficiently with real-time communication. Because of the deep interactions between the cyber and physical systems, the detection of cyberattacks targeting the smart grid has become a challenge in recent years. False data injection (FDI) attacks can bypass the bad data detection algorithm to overflow multiple transmission lines and eventually cause cascading failures or blackouts. In this paper, a simplified neural network is developed to detect FDI attacks targeting transmission line overflows. Compared with the state-of-the-art that mainly focused on stealthy attacks bypassing state-estimation, the novelty of the proposed approach is detecting stealthy attacks that not only bypass the state-estimation, but also result in congestion of transmission lines in smart grids. A bad dataset, created by an attack model, is mixed with a set of clean data to train the proposed detection framework. The numerical results demonstrate high accuracy of this method in detecting cyber–physical attacks. Also, several case studies are included to test the resilience of the proposed method in various scenarios.