Safe and reliable intelligent charging stations are imperative in an intelligent transportation infrastructure. Over the past few years, a big number of smart charging stations have been deployed, and most of them are online and connected, resulting in potential risks of threats. Although there exists related work on securing intelligent vehicles, very little work focused on the security of charging devices. Unlike traditional network systems, these power-related Industrial Control Systems (ICSs) use many different proprietary protocols and diverse interactions. Traditional anomaly detection methods based on network traffic are thus not suitable for these systems. In this work, we propose an anomaly detection method in real vehicle power supply systems based on a deep architecture model. In particular, we propose a novel traffic anomaly detection model based on Multi-Head Attentions (MHA) that take into account the inherent correlations of traffic generated by ICSs. The MHA model is employed to substitute the traditional feature extraction and rule making process with an acceptable computational cost for classifying traffic data. It is an attention-based model that employs Google Transformer encoder architecture to extract recessive features of traffic for anomaly detection. The effectiveness of the model is demonstrated by experiments on two real-world power ICS testbeds including a substation with a slave charging station and a power generation simulation platform based on a distributed control system. Comprehensive experimental results indicate that the MHA model outperforms the Convolutional Neural Networks (CNN)-based and classical machine learning detection models with an accuracy rate of 99.86%.

中文翻译：

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使用多头注意力模型检测智能汽车充电和车站供电系统中的异常

在智能交通基础设施中，安全可靠的智能充电站势在必行。过去几年，大量智能充电站被部署，而且大部分都是在线连接，存在潜在的威胁风险。尽管存在有关保护智能车辆的相关工作，但很少有工作集中在充电设备的安全性上。与传统网络系统不同，这些与电力相关的工业控制系统 (ICS) 使用许多不同的专有协议和各种交互。因此，基于网络流量的传统异常检测方法不适用于这些系统。在这项工作中，我们提出了一种基于深度架构模型的真实车辆电源系统中的异常检测方法。特别是，我们提出了一种基于多头注意（MHA）的新型交通异常检测模型，该模型考虑了 ICS 产生的交通的内在相关性。MHA 模型用于以可接受的计算成本替代传统的特征提取和规则制定过程，以对交通数据进行分类。它是一种基于注意力的模型，它采用 Google Transformer 编码器架构来提取流量的隐性特征以进行异常检测。该模型的有效性通过在两个真实世界电力 ICS 测试平台上的实验证明，包括一个带有从属充电站的变电站和一个基于分布式控制系统的发电仿真平台。