Modern vehicles rely on the Controller Area Network (CAN) bus, whose design prioritizes low cost and real-time performance but provides no message authentication or encryption. An attacker with physical or remote access can therefore inject arbitrary frames, making intrusion detection an important defense-in-depth mechanism. Most published CAN intrusion detection systems rely on presence-based features, such as novel arbitration IDs, frozen payload bytes, or anomalous DLC values. These features perform well on public datasets containing easily separable attacks but fail when attackers reuse legitimate arbitration IDs. We present per-ID behavioral residualization, a CAN-specific representation that extracts fourteen temporal, protocol, and payload features from sliding windows and residualizes them against each arbitration ID's normal baseline. Our central claim is that this representation, rather than any individual detector, drives the performance gains. Across six unsupervised detectors and two datasets, residualization improves mean F1 in the majority of evaluations (21/24 on HCRL and 30/36 on ROAD across five seeds). On the more realistic ROAD dataset, where attacks reuse legitimate IDs, the representation achieves recall >= 0.99 with high ROC-AUC on targeted signal-manipulation attacks. Two limitations are explicitly quantified: novel-ID flooding (HCRL DoS, F1 = 0.02) and cross-ID fuzzing (ROAD, F1 = 0.27), defining the measured coverage boundary of the proposed representation.
Beatrix Koltai, Gergely Acs, Andras Gazdagcs.CR cs.LG
The increasing connectivity of modern vehicles has made securing in-vehicle communication networks a critical challenge. Intrusion Detection Systems (IDS) have been widely studied as a defense mechanism for detecting malicious activities on the Controller Area Network (CAN) bus. However, the evaluation of CAN IDS methods remains difficult due to inconsistencies in experimental setups and the lack of standardized benchmarking frameworks. As a result, reported performance often depends on dataset-specific characteristics and may not reflect how detection methods behave in different environments. This work introduces a benchmarking framework for consistent evaluation of CAN IDSs across multiple datasets. Using the proposed framework, we integrate seven publicly available CAN IDS datasets collected under different experimental conditions and perform cross-dataset evaluation of five conceptually different IDS approaches. Our results highlight how detection performance can vary significantly across datasets, demonstrating the importance of cross-dataset benchmarking for assessing the robustness and generalization capabilities of CAN IDS methods.