EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 79 (2025) ITM Web Conf., 79 (2025) 01031 References
Open Access
Issue
ITM Web Conf.
Volume 79, 2025
International Conference on Knowledge Engineering and Information Systems (KEIS-2025)
Article Number 01031
Number of page(s) 7
DOI https://doi.org/10.1051/itmconf/20257901031
Published online 08 October 2025
  1. B. Lampe, W. Meng, Intrusion detection in the automotive domain: A comprehensive review. IEEE Commun. Surv. Tutor. 25, 2356–2426 (2023). 10.1109/comst.2023.3309864 [Google Scholar]
  2. M. Almehdhar, A. Albaseer, M.A. Khan, M. Abdallah, H. Menouar, S. Al-Kuwari, A. Al-Fuqaha, Deep learning in the fast lane: A survey on advanced intrusion detection systems for intelligent vehicle networks. IEEE Open J. Veh. Technol. 5, 869–906 (2024). 10.1109/ojvt.2024.3422253 [Google Scholar]
  3. E.E. Abdallah, A. Aloqaily, H. Fayez, Identifying intrusion attempts on connected and autonomous vehicles: A survey. Procedia Comput. Sci. 220, 307–314 (2023). https://doi.org/10.1016/j.procs.2023.03.040 [Google Scholar]
  4. J. Nagarajan, P. Mansourian, M.A. Shahid, A. Jaekel, I. Saini, N. Zhang, M. Kneppers, Machine learning based intrusion detection systems for connected autonomous vehicles: A survey. Peer-to- Peer Netw. Appl. 16, 2153–2185 (2023). https://doi.org/10.1007/s12083-023-01508-7 [Google Scholar]
  5. S. Anbalagan, G. Raja, S. Gurumoorthy, R.D. Suresh, K. Dev, IIDS: Intelligent intrusion detection system for sustainable development in autonomous vehicles. IEEE Trans. Intell. Transp. Syst. 24, 15866–15875 (2023). 10.1109/tits.2023.3271768 [Google Scholar]
  6. M. Sorkhpour, A. Yazdinejad, A. Dehghantanha, Auto-CIDS: An autonomous intrusion detection system for vehicular networks. In Proceeding of the Workshop on Autonomous Cybersecurity, Association for Computing Machinery, New York, NY, United States, November 07 (2024), 45–55 [Google Scholar]
  7. S. Anbalagan, G. Raja, S. Gurumoorthy, K. Ayyakannu, Blockchain assisted hybrid intrusion detection system in autonomous vehicles for industry 5.0. IEEE Trans. Consum. Electron. 69, 881–889 (2023). 10.1109/tce.2023.3320282 [Google Scholar]
  8. L. Wang, X. Zhang, D. Li, H. Liu, Multi-sensors space and time dimension based intrusion detection system in automated vehicles. IEEE Trans. Veh. Technol. 73, 200–215 (2023). 10.1109/tvt.2023.3306345 [Google Scholar]
  9. M. Moradi, M. Kordestani, M. Jalali, M. Rezamand, M. Mousavi, A. Chaibakhsh, M. Saif, Sensor and decision fusion-based intrusion detection and mitigation approach for connected autonomous vehicles. IEEE Sens. J. 24, 2090820919 (2024). 10.1109/jsen.2024.3397966 [Google Scholar]
  10. P. Mansourian, N. Zhang, A. Jaekel, M. Kneppers, Deep learning-based anomaly detection for connected autonomous vehicles using spatiotemporal information. IEEE Trans. Intell. Transp. Syst. 24, 16006–16017 (2023). 10.1109/tits.2023.3286611 [Google Scholar]
  11. C. Anthony, W. Elgenaidi, M. Rao, Intrusion detection system for autonomous vehicles using non-tree based machine learning algorithms. Electronics 13, 809 (2024). https://doi.org/10.3390/electronics13050809 [Google Scholar]
  12. L. Westhofen, C. Neurohr, M. Butz, M. Scholtes, M. Schuldes, Using ontologies for the formalization and recognition of criticality for automated driving. IEEE Open J. Intell. Transp. Syst. 3, 519–538 (2022). https://doi.org/10.1109/OJITS.2022.3187247 [Google Scholar]
  13. X. Zhang, J. Sun, Y. Wang, J. Sun, A hierarchical framework for multi-lane autonomous driving based on reinforcement learning. IEEE Open J. Intell. Transp. Syst. 4, 626–638 (2023). 10.1109/ojits.2023.3300748 [Google Scholar]
  14. M. Alkorabi, A. Souri, N. İnanç, Deep learning algorithms for autonomous vehicle communications: Technical insights and open challenges. Concurr. Comput. Pract. Exp. 37, e70218 (2025). https://doi.org/10.1002/cpe.70218 [Google Scholar]
  15. G. Xiong, S. Liu, Y. Yan, Q. Li, H. Li, Efficacy of autonomous vehicle’s adaptive decision-making based on large language models across multiple driving scenarios. IEEE Access. 13, 108076–108092 (2025). https://doi.org/10.1109/access.2025.3580641 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.