Charging Ahead: A Hierarchical Adversarial Framework for Counteracting Advanced Cyber Threats in EV Charging Stations

TL;DR

This paper presents a hierarchical adversarial framework using deep reinforcement learning to detect and counter sophisticated cyberattacks on EV charging stations, improving security against stealthy and advanced threats.

Contribution

It introduces a novel DRL-based hierarchical framework that models both attack strategies and detection mechanisms, enhancing robustness against advanced cyber threats in EV charging infrastructure.

Findings

The framework accurately detects stealthy attacks with low false alarms.

It effectively models advanced attack techniques using DRL.

The approach outperforms existing detection methods in robustness.

Abstract

The increasing popularity of electric vehicles (EVs) necessitates robust defenses against sophisticated cyber threats. A significant challenge arises when EVs intentionally provide false information to gain higher charging priority, potentially causing grid instability. While various approaches have been proposed in existing literature to address this issue, they often overlook the possibility of attackers using advanced techniques like deep reinforcement learning (DRL) or other complex deep learning methods to achieve such attacks. In response to this, this paper introduces a hierarchical adversarial framework using DRL (HADRL), which effectively detects stealthy cyberattacks on EV charging stations, especially those leading to denial of charging. Our approach includes a dual approach, where the first scheme leverages DRL to develop advanced and stealthy attack methods that can bypass basic intrusion detection systems (IDS). Second, we implement a DRL-based scheme within the IDS at EV charging stations, aiming to detect and counter these sophisticated attacks. This scheme is trained with datasets created from the first scheme, resulting in a robust and efficient IDS. We evaluated the effectiveness of our framework against the recent literature approaches, and the results show that our IDS can accurately detect deceptive EVs with a low false alarm rate, even when confronted with attacks not represented in the training dataset.