Detecting State of Charge False Reporting Attacks via Reinforcement Learning Approach

Abstract

The increased push for green transportation has been apparent to address the alarming increase in atmospheric <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${CO}_2$</tex-math> </inline-formula> levels, especially in the last five years. The success and popularity of Electric Vehicles (EVs) have led many carmakers to shift to developing clean cars in the next decade. Moreover, many countries around the globe have set aggressive EV target adoption numbers, with some even aiming to ban gasoline cars by 2050. Unlike their gasoline-based counterparts, EVs comprise many sensors, communication channels, and decision-making components vulnerable to cyberattacks. Hence, the unprecedented demand for EVs requires developing robust defenses against these increasingly sophisticated attacks. In particular, recently proposed cyberattacks demonstrate how malicious owners may mislead EV charging networks by sending false data to unlawfully receive higher charging priorities, congest charging schedules, and steal power. This paper proposes a learning-based detection model that can identify deceptive electric vehicles. The model is trained on an original dataset using real driving traces and a malicious dataset generated from a reinforcement learning agent. The Reinforcement Learning (RL) agent is trained to create intelligent and stealthy attacks that can evade simple detection rules while also giving a malicious EV high charging priority. We evaluate the effectiveness of the generated attacks compared to handcrafted attacks. Moreover, our detection model trained with RL-generated attacks displays greater robustness to intelligent and stealthy attacks.