Inference of Tampering Based on Measurement Discrepancy in EV Charging Station

As electric vehicles (EVs) become more common, the risks and harms of cyber-attacks on EV charging equipment and systems are increasing. The data collection of charging stations and the network of each charging pile may be tampered with by fraudulent consumers or malicious code, resulting in metering discrepancies. Without a systematic reasoning framework, identifying rogue devices or meters can be challenging when there are many compromised endpoints. Fortunately, alarm systems in metering infrastructure can provide operators with decision support tools with quantitative parameters/indicators to identify potential electronic intrusions in charging stations. However, from a security point of view, the interactive implementation of alarms and log data from the distribution network, the charging station, and the charging piles is still underdeveloped. Therefore, this paper proposed an inference for establishing metered network trustworthiness based on changes in the measured values of feeder remote terminal units (FRTU/RTU) and IP-based smart data collection meters (SMs) associated with the root and leaf networks at the charging station, respectively, and validate the inference framework based on the relevant data of an actual charging station.