The rapid global adoption of electric vehicles (EVs) has established electric vehicle supply equipment (EVSE) as a critical component of smart grid infrastructure. While essential for ensuring reliable energy delivery and accessibility, EVSE systems face significant cybersecurity challenges, including network reconnaissance, backdoor intrusions, and distributed denial-of-service (DDoS) attacks. These emerging threats, driven by the interconnected and autonomous nature of EVSE, require innovative and adaptive security mechanisms that go beyond traditional intrusion detection systems (IDS). Existing approaches, whether network-based or host-based, often fail to detect sophisticated and targeted attacks specifically crafted to exploit new vulnerabilities in EVSE infrastructure. This paper proposes a novel intrusion detection framework that leverages multimodal data sources, including network traffic and kernel events, to identify complex attack patterns. The framework employs a distributed learning approach, enabling collaborative intelligence across EVSE stations while preserving data privacy through federated learning. Experimental results demonstrate that the proposed framework outperforms existing solutions, achieving a detection rate above 98% and a precision rate exceeding 97% in decentralized environments. This solution addresses the evolving challenges of EVSE security, offering a scalable and privacypreserving response to advanced cyber threats

翻译：电动汽车在全球范围内的快速普及，已使电动汽车供电设备成为智能电网基础设施的关键组成部分。尽管对于确保可靠的能源输送和可访问性至关重要，但电动汽车供电设备系统面临着严峻的网络安全挑战，包括网络侦察、后门入侵和分布式拒绝服务攻击。这些由电动汽车供电设备互联与自主特性驱动的新兴威胁，要求超越传统入侵检测系统的创新且自适应的安全机制。现有的方法，无论是基于网络的还是基于主机的，通常难以检测那些专门针对电动汽车供电设备基础设施中新漏洞而设计的复杂且有针对性的攻击。本文提出了一种新颖的入侵检测框架，该框架利用包括网络流量和内核事件在内的多模态数据源来识别复杂的攻击模式。该框架采用分布式学习方法，通过联邦学习在实现跨电动汽车供电设备站点协作智能的同时保护数据隐私。实验结果表明，所提出的框架优于现有解决方案，在去中心化环境中实现了超过98%的检测率和超过97%的精确率。该解决方案应对了电动汽车供电设备安全不断演变的挑战，为应对高级网络威胁提供了一种可扩展且保护隐私的响应方案。