Do Can Intrusion Detectors Travel? A Cross-Dataset and Zero-Day Study With Identifier-Agnostic, Locally Calibrated Ensembles

Intrusion detection systems (IDS) for the vehicular CAN bus that use Machine Learning are usually evaluated and reported to achieve very high detection accuracy, commonly above 99%, on random splits of a single dataset. We test and reproduce such high detection accuracy for four publicly available CAN IDS benchmark corpora (OTIDS, Car-Hacking, ROAD, SynCAN), including three real-world vehicles and a synthetic generator, and we expose this number as an artifact of the protocol evaluation used so far. We present a detailed evaluation using a highly effective random-forest detector in different settings, i.e., using different corpora for training and testing. Our results show a significant collapse of a detector’s mean F1 score from near-perfect in-domain performance to 0.345 when transferred to other corpora. Evidence-based identification of this collapse is a significant contribution of this work, a problem which we subsequently fixed by removing identifier one-hot features and using local statistics that are both identifier-agnostic and are locally calibrated on a short window of unlabelled benign traffic of the target-vehicle. We subsequently achieve a significant increase in mean cross-dataset F1 score without using any labelled attack on the target corpus. To the best of our knowledge, this is the first work that (i) identifies the root-cause of this collapse, namely the feature representation, (ii) presents and repairs this feature-representation to recover much of the lost cross-dataset performance using identifier-agnostic statistics that are locally calibrated on minutes of unlabelled benign target-vehicle traffic, and (iii) reports per-family zero-day recall for four publicly available corpora.