A Realtime In-Vehicle Network Testbed for Machine Learning-Based IDS Training and Validation

Hesamaldin Jadidbonab, Andrew Tomlinson, Hoang Nga Nguyen, Trang Doan, Siraj Shaikh

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

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Modern vehicles are built of onboard networked computers, known as Electronic Control Units, to realise a range of functionality and features. Due to their connectivity, they are vulnerable to cyberattacks through various vectors including wired (e.g., CAN, Lin) and wireless (e.g., Bluetooth, WiFi) communications. To facilitate building and validating security measures against these attacks, it is vital to replicate the on-board networks and their components, along with a connected external environment representing required use cases and driving scenarios in an instrumental and controllable environment. In this paper, we propose a multi-component testbed representing a flexible and functional in-vehicle architecture for training and validating machine learning-based Intrusion Detection Systems (IDS).
Original languageEnglish
Title of host publication41st SGAI International Conference on Artificial Intelligence proceedings
PublisherCEUR Workshop Proceedings
Publication statusPublished - 19 Apr 2022
EventWorkshop on AI and Cybersecurity
: co-located with 41st SGAI International Conference on Artificial Intelligence (SGAI 2021)
- Cambridge, United Kingdom
Duration: 14 Dec 202114 Dec 2021

Publication series

NameCEUR Workshop Proceedings
ISSN (Electronic)1613-0073


ConferenceWorkshop on AI and Cybersecurity
Abbreviated title AI-Cybersec 2021
Country/TerritoryUnited Kingdom
Internet address

Bibliographical note

© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)


  • Automotive security
  • testbed
  • machine learning


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  • Secure-CAV

    Nguyen, H. N., Shaikh, S., Bryans, J. & Jadidbonab, H.


    Project: Research

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