标题： 联邦检测开放充电点协议1.6网络攻击 显示英文标题

标题： Federated Detection of Open Charge Point Protocol 1.6 Cyberattacks

摘要： 交通运输部门的持续电气化要求在多个地点部署多个电动汽车（EV）充电站。 然而，由于存在易受攻击的通信协议，如开放充电点协议（OCPP），电动汽车充电站带来了显著的网络物理和隐私风险。 同时，联邦学习（FL）范式展示了一种新的方法，以利用多种物联网数据来源，从而提高入侵检测结果，同时尊重私人信息的机密性。 本文提出采用FL架构对电动汽车充电基础设施进行监控，并检测针对OCPP 1.6协议的网络攻击。 评估结果展示了所提出的基于FL的解决方案的高检测性能。 显示英文摘要

摘要： 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.