How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection

Provenance-based intrusion detection systems (PIDS) frequently report strong performance, but the conclusions drawn from these results can be highly sensitive to benchmarking choices and evaluation protocols. We investigate this dependency by re-evaluating representative PIDS on public datasets that meet our audit, labeling, and calibration requirements. Focusing primarily on the audited DARPA TC E3 datasets, we apply a unified protocol with temporally separated test periods and validation-only checkpoint and threshold calibration, and ask which architectural claims are empirically supported. We find that alerting success and investigation utility can diverge sharply, as several systems surface attacks without providing enough process-level context to support forensic investigation. On three of the four primary datasets, a simple allowlist built from training executable names and paths matches or exceeds the selected learned baselines on key operating-point metrics, suggesting that much of their measured performance reflects lexical novelty rather than richer provenance modeling. To explain why only some datasets expose architectural differences, we measure semantic signal quality through feature completeness and field entropy. This analysis helps explain why several audited E3 datasets can expose alerting behavior without reliably separating model architectures, while Theia pairs the strongest semantic signal quality with the clearest improvements in ranking and node-level recovery by our reference model. These results show that architectural claims in PIDS should be interpreted together with the benchmark properties and evaluation protocol that produced them.

AI-Driven Intrusion Detection Systems (IDS) on the ROAD dataset: A Comparative Analysis for automotive Controller Area Network (CAN)

The integration of digital devices in modern vehicles has revolutionized automotive technology, enhancing safety and the overall driving experience. The Controller Area Network (CAN) bus is a central system for managing in-vehicle communication between the electronic control units (ECUs). However, the CAN protocol poses security challenges due to inherent vulnerabilities, lacking encryption and authentication, which, combined with an expanding attack surface, necessitates robust security measures. In response to this challenge, numerous Intrusion Detection Systems (IDS) have been developed and deployed. Nonetheless, an open, comprehensive, and realistic dataset to test the effectiveness of such IDSs remains absent in the existing literature. This paper addresses this gap by considering the latest ROAD dataset, containing stealthy and sophisticated injections. The methodology involves dataset labelling and the implementation of both state-of-the-art deep learning models and traditional machine learning models to show the discrepancy in performance between the datasets most commonly used in the literature and the ROAD dataset, a more realistic alternative.