Conference Paper2025Cited by 1

IoV Cyberattack Detection via TabTransformer on CAN Bus Communications

Jahanggir Hossain Setu, Armun Alam, Nabarun Halder, Asif Mahmood, Ashraful Islam, M. Ashraful Amin

Innovative Computing 2025, Volume 4

Springer Nature Singapore, pp. 59–65, ISBN: 978-981-96-8011-5

DOI: 10.1007/978-981-96-8011-5_10URL

Abstract

The Internet of Vehicles (IoV) represents a transformative evolution in transportation by linking vehicles through the Internet to improve communication and operational efficiency. This interconnected network relies heavily on the Controller Area Network (CAN) which facilitates communication among Electronic Control Units (ECUs) within vehicles. However, this increased connectivity also introduces vulnerabilities making IoV systems susceptible to various cyberattacks. This study paper focuses on identifying cyberattacks on IoV systems using advanced Machine Learning (ML) techniques. The TabularTransformer (TabTransformer) model was utilized to analyze the CICIoV2024 dataset. To address class imbalance inherent in the dataset, two resampling techniques were implemented: Synthetic Minority Oversampling - Edited Nearest Neighbor (SMOTEENN) and Synthetic Minority Oversampling-Tomek links (SMOTETomek). The comparative study evaluated the performance of the TabTransformer under different class distributions, including a baseline scenario. The results indicate that the SMOTEENN technique improved model performance, achieving a precision of 89%, recall of 85%, and F1-score of 86%.

Keywords

  • SMOTE-Tomek
  • SMOTE-ENN
  • Internet of Vehicles
  • class imbalance
  • TabTransformer
  • cyberattack detection
  • CAN bus
  • electronic control units
  • automotive network security

CCDS Authors

Armun Alam
Armun Alam

Armun Alam

  • Research Assistant, CCDS

Published at

ECCV, Scientific Reports

Research Interests

Machine Learning

Nabarun Halder
Nabarun Halder

Nabarun Halder

  • Research Assistant, CCDS

Published at

UbiComp, IJCNN, HCII, IEEE Access

Research Interests

Machine Learning, Deep Learning, Natural Language Processing, Health Informatics, Human-Computer Interaction

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

  1. 1.Juan Contreras‐Castillo, Sherali Zeadally, Juan Antonio Guerrero Ibáñez. (2017). Internet of Vehicles: Architecture, Protocols, and Security. IEEE Internet of Things Journal, 5(5), 3701–3709[10.1109/jiot.2017.2690902]
  2. 2.Željko Vujović. (2021). Classification Model Evaluation Metrics. International Journal of Advanced Computer Science and Applications, 12(6)[10.14569/ijacsa.2021.0120670]
  3. 3.Hamideh Taslimasa, Sajjad Dadkhah, Euclides Carlos Pinto Neto, Pulei Xiong, Suprio Ray, Ali A. Ghorbani. (2023). Security issues in Internet of Vehicles (IoV): A comprehensive survey. Internet of Things, 22, 100809[10.1016/j.iot.2023.100809]
  4. 4.Euclides Carlos Pinto Neto, Hamideh Taslimasa, Sajjad Dadkhah, Shahrear Iqbal, Pulei Xiong, Taufiq Rahman, Ali A. Ghorbani. (2024). CICIoV2024: Advancing realistic IDS approaches against DoS and spoofing attack in IoV CAN bus. Internet of Things, 26, 101209[10.1016/j.iot.2024.101209]
  5. 5.Azeem Hafeez, Janani Mohan, Mansi Girdhar, S.S. Awad. (2021). Machine Learning based ECU Detection for Automotive Security. , 73–81[10.1109/icenco49852.2021.9698889]
  6. 6.Nur ‘Aliah Amirudin, Said Jadid Abdulkadir. (2024). COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS USING THE CICIOV2024 DATASET. Platform A Journal of Science and Technology[10.61762/pjstvol7iss1art27052]

Cited By

  1. 1.Hao-Shang Ma, Hwa-Young Jeong, Yu-Wei Chan, Hsuan-Che Yang. (2025). Innovative Computing 2025, Volume 4. Lecture notes in electrical engineering[10.1007/978-981-96-8011-5]
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