SSC-IDS: A Robust In-vehicle Intrusion Detection System Based on Self-Supervised Contrastive Learning

As traditional automobiles evolve into the Internet of Vehicles (IoV), the increasingly frequent interactions between intelligent vehicles and external environments make cybersecurity a critical issue. While existing machine learning-based automotive intrusion detection methods demonstrate strong detection performance, most rely on supervised learning frameworks. These approaches not only incur high manual data labeling costs but also show significant limitations when handling real-world, unlabeled attack samples. In this paper, we propose an efficient intrusion detection system for in-vehicle networks based on self-supervised contrastive learning. By leveraging data augmentation, we construct positive sample pairs and learn robust feature representations through joint training using both reconstruction loss and contrastive loss. Additionally, we fuse the features extracted from the backbone and core networks into global representations for downstream classification tasks. Experiments on real in-vehicle intrusion datasets show that SSC-IDS achieves strong performance in anomaly detection. Furthermore, we test the model’s robustness under varying rates of anomaly contamination.