Improved Transformer-based Privacy-Preserving Architecture for Intrusion Detection in Secure V2X Communications

The Internet of Vehicles (IoVs) makes communications between numerous devices that use various protocols susceptible to hacker incursions and attacks, which can compromise privacy and seriously jeopardize driving safety. Many studies have been proposed to detect intrusions hitherto, but two major limitations remain. First, traditional Vehicles-to-Cloud (V2C) have difficulty in figuring out the decentralized distribution of data and computational power in IoVs. Second, the majority of studies suffer from unbalanced data in which the attacks only make up a small part and fail to detect low-probability attacks. To address these limitations, we design a Federated Learning-Edge Cloud (FL-EC) communication architecture for IoVs with a Feature Select Transformer (FSFormer) for effective intrusion detection: In FL-EC, mobile users collect and encrypt data before uploading it to edges for training, with edges and cloud functioning as clients and servers in FL, ensuring privacy and efficient data transmission. In FSFormer, we propose a Feature Attention mechanism to search and promote significant features. Furthermore, the Feed-Forward Network is replaced with a Routing module for a deeper but less-parameter network. Extensive experiments show that our model effectively boosts the detection rate of low-probability attacks and outperforms five baseline models in almost all scenarios.