Novel Intelligent BSM Falsification Attack Detection System Using Trusted Neighbor Vehicle Approach in IoV


  • Hussaini Aliyu Idris
  • Kazunori Ueda
  • Bassem Mokhtar
  • Samir A. Elsagheer Mohamed



Machine learning, Intelligent Transportation System, Misbehavior detection system (MDS), Intrusion detection system (IDS), internet of vehicle (IoV), BSM falsification attack, Deep learning, connected and autonomous vehicles (CAVs)


The proliferation of cyberattacks has emerged as a significant obstacle for advancing technologies such as the Internet of Things (IoT) and Internet of Vehicles (IoV) in recent times. Notably, cryptographic security measures have been implemented in IoV to counteract these cyberattacks. However, these security measures are inadequate when it comes to thwarting internal attackers within the network, as these attackers possess the necessary security credentials for authenticating basic safety messages (BSMs). The research community has made substantial contributions by proposing misbehavior detection systems (MDS) based on data-centric machine learning to identify and prevent internal attackers within IoV. Nevertheless, the existing MDSs in the literature rely on BSMs received from a single vehicle, thereby enabling internal attackers to manipulate their falsified BSMs and evade detection, resulting in a high incidence of false alarms. In this study, we introduce a new intelligent system for detecting falsified BSMs, employing a trusted neighbor vehicle approach (NIBFADS-UTVA)). Our approach demonstrates exceptional effectiveness, achieving an accuracy, precision, recall, and F1-Score all exceeding 99%.


R. S. Rajasekar V., “A study on internet of things devices vulnerabilities using shodan”, International journal of computing, vol. 22(2), pp. 149–158, 2023.

S. Sharma and B. Kaushik, “A survey on internet of vehicles: Applications, security issues & solutions”, Vehicular Communications, vol. 20, article 100182, 2019. DOI: 10.1016/j.vehcom.2019.100182.

S. A. Elsagheer, K. A. Alshalfan, M. A. Al-hagery, and M. T. Ben Othman, “Safe Driving Distance and Speed for Collision Avoidance in Connected Vehicles”, Sensors, vol. 22, no. 18, 2022. DOI: 10.3390/s22187051.

Y. L. Morgan, “Notes on DSRC & WAVE standards suite: Its architecture, design, and characteristics”, IEEE Communications Surveys & Tutorials, vol. 12, no. 4, pp. 504–518, 2010.

D. Jiang, V. Taliwal, A. Meier, W. Holfelder, and R. Herrtwich, “Design of 5.9 GHz DSRC-based vehicular safety communication”, IEEE wireless communications, vol. 13, no. 5, pp. 36–43, 2006.

L. Hobert, A. Festag, I. Llatser, L. Altomare, F. Visintainer, and A. Kovacs, “Enhancements of V2X communication in support of cooperative autonomous driving”,

J. Contreras-Castillo, S. Zeadally, and J. A. GuerreroIbanez, “Internet of Vehicles: Architecture, Protocols, and Security”, IEEE Internet of Things Journal, vol. 5, no. 5, pp. 3701–3709, 2018. DOI: 10.1109/JIOT.2017.2690902.

S. Chen, J. Hu, Y. Shi, et al., “Vehicle-to-Everything (v2x) Services Supported by LTE-Based Systems and 5G”, IEEE Communications Standards Magazine, vol. 1, no. 2, pp. 70–76, 2017. DOI: 10.1109/MCOMSTD.2017.1700015.

B. Setiyono, D. R. Sulistyaningrum, D. W. Wicaksono, et al., “Multi vehicle speed detection using euclidean distance based on video processing”, International journal of computing, vol. 18, no. 4, pp. 431–442, 2019. DOI:

S. A. Elsagheer and K. A. AlShalfan, “Intelligent Traffic Management System Based on the Internet of Vehicles (IoV)”, Journal of Advanced Transportation, vol. 2021, article 4037533, 2021. DOI: 10.1155/2021/4037533.

C. Reshma S, “Optimized Controller Scheme for Autonomous Navigation in Infotainment on Internetof-Vehicles”, International Journal of Computer Networks and Applications (IJCNA), vol. 10(3), pp. 265–276, 2023. DOI: 10.22247/ijcna/2023/221882.

J. B. Kenney, “Dedicated short-range communications (DSRC) standards in the United States”, Proceedings of the IEEE, vol. 99, no. 7, pp. 1162–1182, 2011.

B. Brecht and T. Hehn, “A Security Credential Management System for V2X Communications”, in Connected Vehicles: Intelligent Transportation Systems, R. Miucic, Ed. Springer International Publishing, 2019, pp. 83–115. DOI: 10.1007/978-3-319-94785-3_4.

E. N. ETSI, “302 637-2 v1. 3.1-intelligent transport systems (its); vehicular communications; basic set of applications; part 2: Specification of cooperative awareness basic service”, ETSI, Sept, 2014.

I. Obeidat and M. AlZubi, “Developing a faster pattern matching algorithms for intrusion detection system”, International Journal of Computing, vol. 18, no. 3, pp. 278–284, 2019.

S. Ercan, M. Ayaida, and N. Messai, “Misbehavior Detection for Position Falsification Attacks in VANETs Using Machine Learning”, IEEE Access, vol. 10, pp. 1893–1904, 2022. DOI: 10.1109/ACCESS.2021.3136706.

M. Nabil, A. Hajam, O. Boutkhoum, and A. Haqiq, “Game Theory Application for Misbehavior Detection and Prediction in VANET: Review and Challenges”, International Journal of Computer Networks and Applications (IJCNA), vol. 10(3), pp. 469–482, 2023. DOI: 10.22247/ijcna/2023/221903.

A. Boualouache and T. Engel, “A Survey on Machine Learning-based Misbehavior Detection Systems for

G and Beyond Vehicular Networks”, IEEE Communications Surveys & Tutorials, vol. 25, no. 2, pp. 1128–1172, 2023. DOI: 10.1109/COMST.2023.3236448.

A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks, 2022. DOI: 10.1109/OJVT.2021.3138354.

A. Sharma and A. Jaekel, Machine Learning Based Misbehaviour Detection in VANET Using Consecutive BSM Approach, 2022. DOI: 10.1109/OJVT.2021.3138354.

R. W. van der Heijden, T. Lukaseder, and F. Kargl, “Veremi: A dataset for comparable evaluation of misbehavior detection in vanets”, in Security and Privacy in Communication Networks: 14th International Conference (SecureComm 2018), Springer, Singapore, Aug. 2018, pp. 318–337.

G. O. Anyanwu, C. I. Nwakanma, J. M. Lee, and D.-S. Kim, “Novel hyper-tuned ensemble Random Forest algorithm for the detection of false basic safety messages in Internet of Vehicles”, ICT Express, vol. 9, no. 1, pp. 122–129, 2022. DOI: 10.1016/j.icte.2022.06.003.

G. O. Anyanwu, C. I. Nwakanma, J.-M. Lee, and D.-S. Kim, “Falsification detection system for iov using randomized search optimization ensemble algorithm”, IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 4, pp. 4158–4172, 2023. DOI: 10.1109/TITS.2022.3233536.

G. O. Anyanwu, C. I. Nwakanma, J.-H. Kim, J.-M. Lee, and D.-S. Kim, “Misbehavior Detection in Connected Vehicles using BurST-ADMA Dataset”, in 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), 2022, pp. 874–878. DOI: 10.1109/ICTC55196.2022.9952947.

Y. Wu, L. Wu, and H. Cai, “A deep learning approach to secure vehicle to road side unit communications in intelligent transportation system”, Computers and Electrical Engineering, vol. 105, article 108542, 2023. DOI: 10.1016/j.compeleceng.2022.108542.

M. Alzahrani, M. Y. Idris, F. A. Ghaleb, and R. Budiarto, “An improved robust misbehavior detection scheme for vehicular ad hoc network”, IEEE Access, vol. 10, pp. 111 241–111 253, 2022. DOI: 10.1109/ACCESS.2022.3214838.

M. A. Amanullah, M. Baruwal Chhetri, S. W. Loke, and R. Doss, “BurST-ADMA: Towards an Australian Dataset for Misbehaviour Detection in the Internet of Vehicles”, in The 20th International Conference on Pervasive Computing and Communications, Pisa, Italy, Mar. 2022, pp. 624–629. DOI: 10.1109/PerComWorkshops53856.2022.9767505.

P. A. Lopez, M. Behrisch, L. Bieker-Walz, et al., “Microscopic Traffic Simulation using SUMO”, vol. 2018-Novem, 2018, pp. 2575–2582. DOI: 10.1109/ITSC.2018.8569938.

C. C. Robusto, “The cosine-haversine formula”, The American Mathematical Monthly, vol. 64, no. 1, pp. 38–40, 1957.




How to Cite

Idris, H. A., Ueda, K., Mokhtar, B., & Mohamed, S. A. E. (2024). Novel Intelligent BSM Falsification Attack Detection System Using Trusted Neighbor Vehicle Approach in IoV. International Journal of Computing, 23(1), 116-125.