The increasing connectivity of modern vehicles exposes the in-vehicle controller area network (CAN) bus to various cyberattacks, including denial-of-service, fuzzy injection, and spoofing attacks. Existing machine learning and deep learning intrusion detection systems (IDS) often rely on labeled data, struggle with class imbalance, lack interpretability, and fail to generalize well across different datasets. This paper proposes a lightweight and interpretable IDS framework based on non-negative matrix factorization (NMF) to address these limitations. Our contributions include: (i) evaluating NMF as both a standalone unsupervised detector and an interpretable feature extractor (NMF-W) for classical, unsupervised, and deep sequence models; (ii) providing comprehensive benchmarking on the car-hacking dataset (CHD), demonstrating improved robustness in mixed-attack and cross-attack scenarios, with class imbalance addressed through oversampling and class weighting; (iii) offering a component-level interpretability analysis that links NMF factors to meaningful CAN traffic patterns; and (iv) validating cross-dataset transferability on the offset-ratio and time interval-based intrusion detection system (OTIDS) dataset. Additional ablation and efficiency studies confirm the practical feasibility of deploying NMF-based IDS on embedded automotive controllers. Overall, this work presents a balanced IDS solution that combines detection accuracy, computational efficiency, and explainability, thereby advancing the security of in-vehicle networks.
Balasubramaniam et al. (Thu,) studied this question.