The electric vehicle (EV) charging ecosystem, being a distinguishable paradigm of IIoT infrastructure, consists of distributed and complex hybrid systems that demand adaptive data-driven cyber-defense mechanisms to tackle the ever-growing attack vectors of cyber-physical systems. We propose an adaptive differential privacy-based federated learning framework for building a collaborative network intrusion detection system model for EV charging stations (EVCS). We use utility optimized local differential privacy to provide data privacy to the local network traffic data of each EVCS. Moreover, we propose a reinforcement learning-based intelligent privacy allocation mechanism at the EVCS level. The main significance of the proposed mechanism is that it can make privacy provisioning adaptive to the extent of privacy breaching rate, and dynamically optimize the privacy budget and the utility to avoid human intervention such as domain knowledge experts. The experimental results confirm the efficacy of our proposed mechanism and achieves appropriate privacy provisioning accuracy to approximately 95%.

中文翻译：

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电动汽车充电基础设施智能隐私保护方案


电动汽车 (EV) 充电生态系统是工业物联网基础设施的一个独特范例，由分布式复杂的混合系统组成，需要自适应数据驱动的网络防御机制来应对网络物理系统不断增长的攻击向量。我们提出了一种基于自适应差分隐私的联合学习框架，用于构建电动汽车充电站（EVCS）的协作网络入侵检测系统模型。我们使用效用优化的本地差分隐私为每个 EVCS 的本地网络流量数据提供数据隐私。此外，我们在 EVCS 级别提出了一种基于强化学习的智能隐私分配机制。该机制的主要意义在于，它可以使隐私供应适应隐私泄露率的程度，并动态优化隐私预算和效用，以避免领域知识专家等人为干预。实验结果证实了我们提出的机制的有效性，并实现了约 95% 的适当隐私配置准确度。