Safeguarding the Future of Mobility: Cybersecurity Issues and Solutions for Infrastructure Associated with Electric Vehicle Charging

TL;DR

This paper discusses the importance of cybersecurity for electric vehicle charging infrastructure, proposing a machine learning-based intrusion detection system to enhance security with low false positives and high accuracy.

Contribution

It introduces a novel machine learning-based intrusion detection system specifically designed to protect EV charging station ecosystems.

Findings

The proposed IDS achieves high detection accuracy.

The system maintains low false-positive rates.

It effectively safeguards EV charging infrastructure.

Abstract

The development of an ecosystem that balances consumer convenience and security is imperative given the expanding market for electric vehicles (EVs). The vast amount of data that EV charging station management systems (EVCSMSs) give is powered by the Internet of Things (IoT) ecosystem. Intrusion Detection Systems (IDSs), which track network traffic to spot potentially dangerous data exchanges in IT and IoT contexts, are constantly improving in terms of efficacy and accuracy. Intrusion detection is becoming a major topic in academia because of the acceleration of IDS development caused by machine learning and deep learning techniques. The goal of the research presented in this paper is to use a machine-learning-based intrusion detection system with low false-positive rates and high accuracy to safeguard the ecosystem of EV charging stations (EVCS).