LFD-IDS: bagging-based data poisoning attacks against cyberattack detection in connected vehicle

Pooranian, Z., Taheri, R. and Martinelli, F. (2025) LFD-IDS: bagging-based data poisoning attacks against cyberattack detection in connected vehicle. IEEE Transactions on Intelligent Transportation Systems. ISSN 1558-0016 doi: 10.1109/TITS.2025.3581102

Abstract/Summary

This paper explores the need for new systems to detect and monitor cyberattacks in Connected Vehicles (CVs). Sensor health in CVs is vital, as prediction errors and communication issues can weaken the sensor network. Intrusion Detection Systems (IDS) for CVs must be continuously updated to meet changing needs and be robust against adversarial attacks. We developed a new Label Flipping system against Deep learning-based IDS (LFD-IDS) to help cloud operators understand unusual vehicle sensor data. LFD-IDS specifically targets detecting and explaining sensor data manipulation from poisoning attacks. We proposed two label-flipping attacks based on Bootstrapping and Bagging and a defensive strategy using a multi-layer deep neural network. Our LFD-IDS achieves at least 90% accuracy in identifying cyberattacks.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/123472
Identification Number/DOI	10.1109/TITS.2025.3581102
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords	Connected vehicle, machine learning, adversarial attacks, cyberattack, intrusion detection systems (IDS).
Publisher	IEEE
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