Federated Detection of Open Charge Point Protocol 1.6 Cyberattacks

TL;DR

This paper proposes a federated learning approach to detect cyberattacks on EV charging stations using the OCPP 1.6 protocol, enhancing security while preserving data privacy across distributed infrastructure.

Contribution

It introduces a novel federated learning framework specifically designed for detecting cyberattacks on EV charging stations using OCPP 1.6, addressing privacy and security challenges.

Findings

High detection accuracy demonstrated in experiments

Effective privacy preservation across distributed data sources

Potential for real-world deployment in EV infrastructure

Abstract

The ongoing electrification of the transportation sector requires the deployment of multiple Electric Vehicle (EV) charging stations across multiple locations. However, the EV charging stations introduce significant cyber-physical and privacy risks, given the presence of vulnerable communication protocols, like the Open Charge Point Protocol (OCPP). Meanwhile, the Federated Learning (FL) paradigm showcases a novel approach for improved intrusion detection results that utilize multiple sources of Internet of Things data, while respecting the confidentiality of private information. This paper proposes the adoption of the FL architecture for the monitoring of the EV charging infrastructure and the detection of cyberattacks against the OCPP 1.6 protocol. The evaluation results showcase high detection performance of the proposed FL-based solution.