The increasing connectivity among vehicles along with their rising complexity increases their attack surface and challenges their security. In this paper, we consider the problem of intrusion detection for SOME/IP protocol and present “SAID” a novel technique for the detection of anomalies from a large sequence of exchanged SOME/IP network packets. The proposed detector leverages a self-attention-based neural network to model the contextual dependencies between SOME/IP packets. For this purpose, we evaluate our proposed approach, by generating a simulated and manually annotated SOME/IP dataset, with several categories of attacks. The results of the extensive experiments indicate that our technique detects (with high accuracy) the majority of SOME/IP’s protocol violations, e.g., with an area-under-the-curve ≈0.8, and inference time ≈0.3 ms. A comparative study, including various state-of-the-art benchmark algorithms, shows that SAID shows better performance in detecting intrusions and enables parallelization. Our source code and data are available at: https://github.com/Alkhatibnatasha/supervised_detection_some_ip/