Automotive Ethernet carries heterogeneous multi-protocol traffic in modern in-vehicle networks, where labeled attack data are rarely available and the strongest prior unsupervised detector still relies on handcrafted traffic features. This article presents X-SPUR, an explainable, surprisal-based, protocol-aware unsupervised reasoning framework that instead represents raw packet fields as token sequences, learns benign traffic patterns through causal language modeling, and detects anomalies from per-token cross-entropy surprisal. To incorporate temporal context, we introduce a bimodal fusion architecture that combines payload-token embeddings with inter-packet timing through additive fusion and a Hadamard interaction. To handle the heterogeneous score distributions of different protocol families, we further propose a dual top-$k$% per-protocol $Z$-score calibration that jointly captures moderately distributed and sparse anomaly signatures. On the TOW-IDS dataset, X-SPUR achieves an AUC of 0.9987. This is marginally higher than the 0.9969 reported for AERO. X-SPUR also eliminates handcrafted feature engineering. We train a separate CarDS model using the same architecture and training hyperparameters. This model retains strong performance on the second automotive Ethernet dataset. Beyond detection, per-token surprisal provides fine-grained explainability by attributing anomaly scores to specific protocol fields, supporting interpretable security analysis in heterogeneous in-vehicle networks.

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