Title: NAIBI: a neighbour-vehicle approach for anomaly detection of basic safety message falsification in internet of vehicles using machine learning

Authors: Hussaini Aliyu Idris; Kazunori Ueda; Bassem Mokhtar; Samir A. Elsagheer Mohamed

Addresses: Department of Computer Science and Engineering, Egypt-Japan University of Science and Technology (E-JUST), 21934, Alexandria, Egypt ' Department of Computer Science and Engineering, Waseda University, Tokyo, Japan ' Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt; College of Information Technology, UAE University, Al Ain 15551, UAE ' Department of Computer Science and Engineering, Egypt-Japan University of Science and Technology (E-JUST), 21934, Alexandria, Egypt; Faculty of Engineering, Aswan University, Aswan, Egypt

Abstract: The alarming rate of fatality and injuries recorded through road accidents call for the deployment of intelligent transportation system (ITS). The internet of vehicles (IoV), being the backbone of ITS, provides vehicles with standards and protocols to disseminate basic safety messages (BSMs) containing kinematic information to other vehicles and infrastructures, making the IoV a complex network and therefore susceptible to cyberattacks. Despite employing public-key infrastructure (PKI) to ensure BSMs are digitally signed and authenticated, insider attackers can still falsify BSMs and cause chaos in the network. The research community has contributed by proposing data-centric approaches however, the over-reliance on one vehicle BSM data for training and inference gives the attacker an upper hand. To address these drawbacks, we proposed a machine learning-based neighbour-vehicle approach for anomaly detection of BSM falsification in IoV (NAIBI) and demonstrate its superiority over the state-of-the-art which exceeds 99% in accuracy, precision, recall and F1-score.

Keywords: machine learning; intelligent transportation system; ITS; misbehaviour detection system; MDS; internet of vehicle; IoV; BSM falsification attack.

DOI: 10.1504/IJICS.2025.146612

International Journal of Information and Computer Security, 2025 Vol.27 No.1, pp.68 - 92

Received: 07 Nov 2023
Accepted: 23 Mar 2024
Published online: 06 Jun 2025 *

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