| Summary: | Software-defined vehicles (SDVs) make automotive systems more intelligent and adaptable, and this transformation relies on hybrid automotive in-vehicle networks that refer to multiple protocols using automotive Ethernet (AE) or a controller area network (CAN). Numerous researchers have developed specific intrusion-detection systems (IDSs) based on ResNet18, VGG16, and Inception for AE or CANs, to improve confidentiality and integrity. Although these IDSs can be extended to hybrid automotive in-vehicle networks, these methods often overlook the requirements of real-time processing and minimizing of the false positive rate (FPR), which can lead to safety and reliability issues. Therefore, we introduced an IDS based on the Swin Transformer to bolster hybrid automotive in-vehicle network reliability and security. First, multiple messages from the traffic assembly are transformed into images and compressed via two-dimensional wavelet discrete transform (2D DWT) to minimize parameters. Second, the Swin Transformer is deployed to extract spatial and sequential features to identify anomalous patterns with its attention mechanism. To compare fairly, we re-implemented up-to-date conventional network models, including ResNet18, VGG16, and Inception. The results showed that our method could detect attacks with 99.82% accuracy and 0 FPR, which saved 14.32% in time costs and improved the accuracy by 1.60% compared to VGG16 when processing 512 messages.
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