Detecting Anomalies in Intelligent Vehicle Charging and Station Power Supply Systems With Multi-Head Attention Models

Abstract

Safe and reliable intelligent charging stations are imperative in an intelligent transportation infrastructure. Over the past few years, a big number of smart charging stations have been deployed, and most of them are online and connected, resulting in potential risks of threats. Although there exists related work on securing intelligent vehicles, very little work focused on the security of charging devices. Unlike traditional network systems, these power-related Industrial Control Systems (ICSs) use many different proprietary protocols and diverse interactions. Traditional anomaly detection methods based on network traffic are thus not suitable for these systems. In this work, we propose an anomaly detection method in real vehicle power supply systems based on a deep architecture model. In particular, we propose a novel traffic anomaly detection model based on Multi-Head Attentions (MHA) that take into account the inherent correlations of traffic generated by ICSs. The MHA model is employed to substitute the traditional feature extraction and rule making process with an acceptable computational cost for classifying traffic data. It is an attention-based model that employs Google Transformer encoder architecture to extract recessive features of traffic for anomaly detection. The effectiveness of the model is demonstrated by experiments on two real-world power ICS testbeds including a substation with a slave charging station and a power generation simulation platform based on a distributed control system. Comprehensive experimental results indicate that the MHA model outperforms the Convolutional Neural Networks (CNN)-based and classical machine learning detection models with an accuracy rate of 99.86%.