The integration of several developing technologies and their applications with Internet of Vehicles (IoVs) techniques has been improved. Utilizing these emerging technologies renders the in-vehicle network more susceptible to intrusions. Furthermore, the utilization of Electronic Control Units (ECUs) in current vehicles has experienced a significant increase, establishing the Controller Area Network (CAN) as the widely used standard in the automotive field. The CAN protocol provides an efficient and broadcast-based protocol for facilitating serial data exchange between ECUs. However, it lacks provisions for security measures such as authentication and encryption. The attackers have exploited these weaknesses to launch various attacks on CAN-based IVN. This paper proposes STC-GraphFormer, an innovative spatial-temporal model that utilizes a Graph Convolutional Network (GCN) and a transformer. The spatial GCN layers are utilized to construct and acquire local spatial features, while the temporal transformer layers are employed to capture the long-term global temporal dependencies. By employing this integrated approach, STC-GraphFormer can learn complex spatial-temporal correlations within the IVN data, enabling it to detect and classify malicious intrusions. The proposed STC-GraphFormer has been validated using five real in-vehicle CAN datasets that cover a wide range of attacks that have not been previously investigated together. The finding results indicate that the STC-GraphFormer is more efficient than the SOTA approaches. It demonstrates excellent performance, with Car-hacking (0.99983), IVN intrusion detection (0.9991), CAN Dataset for intrusion detection “OTIDS” (0.9992), CAR hacking: attack & defense challenge (0.9901), and Survival analysis (0.9982), with a minimal false alarm rate and the highest achievable F1 scores for various types of attacks.

中文翻译：

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STC-GraphFormer： 用于车载网络入侵检测系统的图时空相关变压器


几种开发中的技术及其应用与车联网 （IoVs） 技术的集成得到了改进。利用这些新兴技术使车载网络更容易受到入侵。此外，电子控制单元 （ECU） 在当前车辆中的使用显着增加，使控制器局域网 （CAN） 成为汽车领域广泛使用的标准。CAN 协议提供了一种高效的基于广播的协议，用于促进 ECU 之间的串行数据交换。但是，它缺乏对身份验证和加密等安全措施的规定。攻击者利用这些弱点对基于 CAN 的 IVN 发起各种攻击。本文提出了 STC-GraphFormer，这是一种利用图卷积网络 （GCN） 和变压器的创新时空模型。空间 GCN 层用于构建和获取局部空间特征，而时间转换器层用于捕获长期的全局时间依赖关系。通过采用这种集成方法，STC-GraphFormer 可以学习 IVN 数据中复杂的时空相关性，使其能够检测和分类恶意入侵。拟议的 STC-GraphFormer 已使用五个真实的车载 CAN 数据集进行了验证，这些数据集涵盖了以前未一起调查过的广泛攻击。发现结果表明，STC-GraphFormer 比 SOTA 方法更有效。它展示了出色的性能，包括汽车黑客攻击（0.99983）、IVN入侵检测（0.9991）、CAN入侵检测数据集“OTIDS”（0.9992）、CAR黑客攻击：攻击与防御挑战（0.9901）和生存分析（0.9982），对于各种类型的攻击，误报率最低，F1 分数最高。