Detecting Energy Theft Attacks on an Off-Grid Charging Station
Résumé
With the rapid development of charging infrastructure for Electric Vehicles, the risks of cyber-physical attacks, including energy theft are growing. The attack detection results of energy theft are usually validated on real open access data of charging sessions, however, the attacks themselves are artificially introduced. To address this issue, this work presents a three-week experiment on a real testbed including the production and consumption of energy with realistic energy theft attacks occurring in the system. The energy setup emulates a charging bike station where users can charge bikes at different levels of state of charge and at different durations of charging sessions. The attacker is one of the users who steals energy from the system for its own bike and can override the reported consumption of power. We propose a method for detecting such attacks based on the total production/consumption power balance. The full dataset of the three-week experiment is published with this work for reproducibility purposes.
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