Article Sidebar

Submitted
April 6, 2025
Published
September 30, 2024
Download
pdf
Statistic
Read Counter : 43 Download : 37

Main Article Content

Abstract

The implementation of Internet of Things (IoT) technology in motor vehicles has been increasing over time and is known as the Internet of Vehicles (IoV). IoV is becoming more essential to society as it provides comfort, safety, and efficiency in driving. Unfortunately, the use of internet technology in IoV brings the potential for cyber-attacks, such as Denial of Service (DoS) and Spoofing. Intrusion Detection Systems in IoV have not yet fully matured, as this technology is still relatively new. Therefore, the potential threats and their significant impact make research on this topic urgently needed. This study aims to evaluate the performance of the k-Nearest Neighbor (kNN) classification algorithm in detecting cyber-attacks on IoV. The predicted classes in this study consist of six categories: Benign, DoS, Gas-Spoofing, Steering Wheel-Spoofing, Speed-Spoofing, and RPM-Spoofing. These two types of attacks on IoV (DoS and Spoofing) pose risks to the operational safety of vehicles, which can endanger drivers and other road users. The dataset used is a public dataset called CIC IoV2024. The performance of the kNN algorithm is also compared to three other state-of-the-art algorithms, including Naïve Bayes, Deep Neural Network, and Random Forest. The results show that k-Nearest Neighbor (kNN) achieved the best performance with a score of 98.7% for both accuracy and F1-Score metrics. kNN outperformed Naïve Bayes, which ranked second with a score of 98.1% accuracy and 98.0% F1-Score. Thus, the kNN algorithm can be recommended as a classifier in the development of an intrusion detection system for IoV

Keywords

Internet of Vehicles Denial of service kNN Spoofing

Article Details

References

  1. Ahmed, I., Jeon, G. and Ahmad, A. (2023) ‘Deep Learning-Based Intrusion Detection System for Internet of Vehicles’, IEEE Consumer Electronics Magazine, 12(1), pp. 117–123. Available at: https://doi.org/10.1109/MCE.2021.3139170.
  2. Battineni, G. et al. (2019) ‘Comparative Machine-Learning Approach: A Follow-Up Study on Type 2 Diabetes Predictions by Cross-Validation Methods’, Machines, 7(4), p. 74. Available at: https://doi.org/10.3390/machines7040074.
  3. Chen, M. et al. (2024) ‘An attribute-encryption-based cross-chain model in urban internet of vehicles’, Computers and Electrical Engineering, 115, p. 109136. Available at: https://doi.org/10.1016/j.compeleceng.2024.109136.
  4. Chung, W. and Cho, T. (2022) ‘Complex attack detection scheme using history trajectory in internet of vehicles’, Egyptian Informatics Journal, 23(3), pp. 499–510. Available at: https://doi.org/10.1016/j.eij.2022.05.002.
  5. Dev, S. et al. (2022) ‘Performance Analysis and Prediction of Diabetes using Various Machine Learning Algorithms’, in 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India: IEEE, pp. 517–521. Available at: https://doi.org/10.1109/ICAC3N56670.2022.10074117.
  6. Djenouri, Y. et al. (2024) ‘Enhancing smart road safety with federated learning for Near Crash Detection to advance the development of the Internet of Vehicles’, Engineering Applications of Artificial Intelligence, 133, p. 108350. Available at: https://doi.org/10.1016/j.engappai.2024.108350.
  7. Gupta, G., Rai, A. and Jha, V. (2021) ‘Predicting the Bandwidth Requests in XG-PON System using Ensemble Learning’, in 2021 International Conference on Information and Communication Technology Convergence (ICTC). 2021 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, Republic of: IEEE, pp. 936–941. Available at: https://doi.org/10.1109/ICTC52510.2021.9620935.
  8. Haodudin Nurkifli, E. and Hwang, T. (2023) ‘Provably secure authentication for the internet of vehicles’, Journal of King Saud University - Computer and Information Sciences, 35(8), p. 101721. Available at: https://doi.org/10.1016/j.jksuci.2023.101721.
  9. Islam, S. et al. (2022) ‘State-of-the-art vehicle-to-everything mode of operation of electric vehicles and its future perspectives’, Renewable and Sustainable Energy Reviews, 166, p. 112574. Available at: https://doi.org/10.1016/j.rser.2022.112574.
  10. Kaur, G. and Garg, H. (2023) ‘A novel algorithm for autonomous parking vehicles using adjustable probabilistic neutrosophic hesitant fuzzy set features’, Expert Systems with Applications, 226, p. 120101. Available at: https://doi.org/10.1016/j.eswa.2023.120101.
  11. Korium, M.S. et al. (2024) ‘Intrusion detection system for cyberattacks in the Internet of Vehicles environment’, Ad Hoc Networks, 153, p. 103330. Available at: https://doi.org/10.1016/j.adhoc.2023.103330.
  12. Neto, E.C.P. et al. (2024) ‘CICIoV2024: Advancing realistic IDS approaches against DoS and spoofing attack in IoV CAN bus’, Internet of Things, 26, p. 101209. Available at: https://doi.org/10.1016/j.iot.2024.101209.
  13. Orrù, G. et al. (2020) ‘Machine Learning in Psychometrics and Psychological Research’, Frontiers in Psychology, 10, p. 2970. Available at: https://doi.org/10.3389/fpsyg.2019.02970.
  14. Qureshi, K.N. et al. (2021) ‘Internet of Vehicles: Key Technologies, Network Model, Solutions and Challenges With Future Aspects’, IEEE Transactions on Intelligent Transportation Systems, 22(3), pp. 1777–1786. Available at: https://doi.org/10.1109/TITS.2020.2994972.
  15. Rafrastara, F.A. et al. (2023) ‘Deteksi Malware menggunakan Metode Stacking berbasis Ensemble’, Jurnal Informatika, 8(1), pp. 11–16.
  16. Rafrastara, F.A. et al. (2024) ‘Performance Comparison of k-Nearest Neighbor Algorithm with Various k Values and Distance Metrics for Malware Detection’, 8.
  17. Rafrastara, F.A., Ghozi, W. and Wardoyo, A. (2024) ‘Deteksi Serangan berbasis Machine Learning pada Internet of Vehicle’, in IN-FEST 2024. IN-FEST 2024: Seminar Nasional Informatika - FTI UPGRIS, UPGRIS Semarang: UPGRIS Semarang.
  18. Sherazi, H.H.R. et al. (2019) ‘DDoS attack detection: A key enabler for sustainable communication in internet of vehicles’, Sustainable Computing: Informatics and Systems, 23, pp. 13–20. Available at: https://doi.org/10.1016/j.suscom.2019.05.002.
  19. Supriyanto, C. et al. (2024) ‘Malware Detection Using K-Nearest Neighbor Algorithm and Feature Selection’, 8.
  20. University of New Brunswick (2024) ‘CIC IoV dataset 2024: Advancing Realistic IDS Approaches against DoS and Spoofing Attack in IoV CAN bus’. Available at: https://www.unb.ca/cic/datasets/iov-dataset-2024.html (Accessed: 9 June 2024).
  21. Wang, M. and Wang, S. (2021) ‘Communication Technology and Application in Internet of Vehicles’, in 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE). 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE), Dalian, China: IEEE, pp. 234–237. Available at: https://doi.org/10.1109/ICISCAE52414.2021.9590660.
  22. Wei, X. (2024) ‘Enhancing road safety in internet of vehicles using deep learning approach for real-time accident prediction and prevention’, International Journal of Intelligent Networks, 5, pp. 212–223. Available at: https://doi.org/10.1016/j.ijin.2024.05.002.