CAN We Trust Your Results? A Cross-Dataset Study of Automotive IDS Evaluation
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 multi...
| Authors | Beatrix Koltai, Gergely Acs, Andras Gazdag |
|---|---|
| Primary category | cs.CR |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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