Adversarial Machine Learning on Automotive Attack Surfaces: Threats, Intrusion Detection, and Zero-Knowledge Defenses

Abstract

Modern vehicles are distributed embedded computing platforms whose expanding network connectivity-CAN bus, Bluetooth, cellular telematics, and over-the-air (OTA) update channels-exposes them to the same class of adversarial attacks studied in cloud and enterprise environments. Machine learning (ML)-based intrusion detection systems (IDS) have emerged as the primary defensive response, yet these models are themselves vulnerable to adversarial perturbation: a well-crafted malicious CAN frame can e

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@misc{ologunde2026adversarial,
  title = {{Adversarial Machine Learning on Automotive Attack Surfaces: Threats, Intrusion Detection, and Zero-Knowledge Defenses}},
  author = {Ezekiel Ologunde},
  year = {2026},
  doi = {10.2139/ssrn.6278899},
  url = {https://doi.org/10.2139/ssrn.6278899}
}