False Data Injection Attack Detection in EV Charging Network using NARX Neural Network

The cyber threats in Electric Vehicle (EV) charging networks have become prevalent and targeting vehicle’s charging process and power supply from grid. It has been observed that the existing state of the art schemes for False Data Injection (FDI) attack detection cannot detect the injection which is random or unpredictable in nature and persists for short time interval. Most importantly, it remains undetected when the impact of injection mimics the natural behaviour of EV charging process. Therefore, in this paper, a Deep Learning (DL) based FDI attack detection scheme is proposed for EV charging network. The Non-linear Autoregressive Exogeneous input (NARX) Neural Network (NN) is used to estimate the energy (kWh) delivered to an EV during its charging session. The Error of Estimation (EoE) obtained from the sensed and estimated values is further analysed using Inter-Quartile Range (IQR) technique and the attack is detected by identifying few consecutive spikes given by IQR. The proposed attack detection method is evaluated using a real-world EV charging dataset and compared with the existing state of the art attack detection scheme. The simulation results indicate that the proposed attack detection scheme outperforms the other schemes by achieving attack detection accuracy of 99.40% whereas the existing schemes give 88.68%, and 98% accuracy respectively.