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Machine Learning for Security in Vehicular Networks: A Comprehensive Survey
Open accessAbstract
Machine Learning (ML) has emerged as an attractive and viable technique to provide effective solutions for a wide range of application domains. An important application domain is vehicular networks wherein ML-based approaches are found to be very useful to address various problems. The use of wireless communication between vehicular nodes and/or infrastructure makes it vulnerable to different types of attacks. In this regard, ML and its variants are gaining popularity to detect attacks and deal with different kinds of security issues in vehicular communication. In this paper, we present a comprehensive survey of ML-based techniques for different security issues in vehicular networks. We first briefly introduce the basics of vehicular networks and different types of communications. Apart from the traditional vehicular networks, we also consider modern vehicular network architectures. We propose a taxonomy of security attacks in vehicular networks and discuss various security challenges and requirements. We classify the ML techniques developed in the literature according to their use in vehicular network applications. We explain the solution approaches and working principles of these ML techniques in addressing various security challenges and provide insightful discussion. The limitations and challenges in using ML-based methods in vehicular networks are discussed. Finally, we present observations and lessons learned before we conclude our work.
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“The adoption of AI in IoV applications raises privacy concerns due to the large amounts of data collected, transmitted and stored from vehicles, passengers, infrastructure, and pedestrians [88, 134].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [134]
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In the citing paper
“To guarantee data security, IoV experts must ensure the data are authenticated and made confidential. Other requirements include integrity, authorization, and nonrepudiation [57].”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [57]
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“However, IoV data includes sensitive and private information such as location details, driving patterns, and personally identifiable information, raising significant privacy concerns regarding its collection, use, and potential misuse or breaches [88, 134].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [134]
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“In the context of IoV, several survey papers provided a broad overview of privacy and security challenges such as security in intelligent transportation systems (ITS) [43], [129], machine learning for vehicular network security [134], PPML for electric vehicles (EVs) [121], and security in 6G-enabled IoV [98].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [134]
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“While IoV is vulnerable to multiple threats, researchers have identified some of the most vulnerable areas and how they can be protected from network attacks [57,58,59,60].”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [57]
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“Despite its strengths, ML faces a range of challenges such as failure in the detection of attacks, wrongly classified objects, the recognition of driver monitoring patterns, vehicle theft, and compromised functional safety [57,76,171,172].”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [57]
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
Altman, N. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475879 · Cited by 5,657
Artificial neural networks: a tutorial
Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996) · Computer · doi:10.1109/2.485891 · Cited by 5,095
In-Datacenter Performance Analysis of a Tensor Processing Unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al. (2017) · Proceedings of the 44th Annual International Symposium on Computer Architecture · doi:10.1145/3079856.3080246 · Cited by 4,537
Flagged first, then by citations
3 of the 220 references couldn’t be matched to a known paper.
Research works printed but not linked: 10
- 2006, NGSIM Dataset
- 1999, Kdd Cup 1999 Dataset
- 10.1109/JIOT.2018.2820039 · DOI
- liang, 2018, Deep reinforcement learning for traffic light control in vehicular networks, arXiv 1803 11115
- kleinbaum, 2002, Logistic Regression
- 10.1109/TITS.2019.2962338 · DOI · Paper page
- 10.1109/COMST.2020.2986444 · DOI · Paper page
- öztürk, 2012, Driver Status Identification from Driving Behavior Signals, 31
- anderson, 1992, Artificial neural networks technology, 258, 1
- 10.1109/GLOCOM.2018.8647927 · DOI · Paper page
Cited by
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Chen, L. F., Li, Y., Huang, C., Xing, Y., Tian, D., Li, L., Hu, Z., Teng, S., Lv, C., Wang, J., et al. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3276218 · Cited by 146
Alalwany, E., & Mahgoub, I. (2024) · Sensors · doi:10.3390/s24020368 · Cited by 130
Anyanwu, G. O., Nwakanma, C. I., Lee, J., & Kim, D. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3199712 · Cited by 119
Yazıcı, İ., Shayea, I. A. M., & Din, J. (2023) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2023.101455 · Cited by 146
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Abdel Hakeem, S. A., & Kim, H. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3558849 · Cited by 90
Rizvi, S., Scanlon, M., McGibney, J., & Sheppard, J. (2022) · IEEE Access · doi:10.1109/access.2022.3214506 · Cited by 87
Physical Layer Authentication and Security Design in the Machine Learning Era
Hoang, T. M., Vahid, A., Tuan, H. D., & Hanzo, L. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3363639 · Cited by 73
Zhang, S., Zhu, D., & Liu, Y. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110255 · Cited by 60
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Han, J., Ju, Z., Chen, X., Yang, M., Zhang, H., & Huai, R. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3304762 · Cited by 57
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Ding, W., Alrashdi, I., Hawash, H., & Abdel‐Basset, M. (2023) · Information Sciences · doi:10.1016/j.ins.2023.120057 · Cited by 37
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Yang, T., Sun, R., Rathore, R. S., & Baig, I. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj16010014 · Cited by 23
Alharthi, M., Medjek, F., & Djenouri, D. (2025) · Future Internet · doi:10.3390/fi17070317 · Cited by 23
Gupta, M. S., Srivastava, A., & Kumar, K. (2025) · Vehicular Communications · doi:10.1016/j.vehcom.2025.100882 · Cited by 20
Parra, O. J. S., Sánchez, L. C., & Gómez, J. G. (2025) · IEEE Access · doi:10.1109/access.2025.3548640 · Cited by 19
Song, C., Wu, J., Xian, K., Huang, J., & Lü, L. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110676 · Cited by 18
Xu, T., Wang, N., Pang, Q., & Zhao, X. (2024) · Scalable Computing: Practice and Experience · doi:10.12694/scpe.v25i2.2629 · Cited by 17
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Tu, J., & Shang, W. (2023) · International Conference on Sensing, Measurement, Communication and Internet of Things Technologies (SMC-IoT) · doi:10.1109/smc-iot62253.2023.00044 · Cited by 7
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Wang, Q., Ren, X., Li, L., & Pen, H. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.01506131 · Cited by 2
Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 9
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Fu, Q., Yu, M., Hui, F., & Liu, J. (2025) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2025-spring65109.2025.11174755 · Cited by 1
Wan, D., & Huang, J. X. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3587612 · Cited by 1
Ragunthar, T., Christila, S. A., Jyoshna, B., Meenachi, L., Das, S., & Patro, P. (2026) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70363 · Cited by 1
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Impact of Intelligent Technologies on IoV Security: Integrating Edge Computing and AI
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Zhukabayeva, T. K., Ahmad, Z., Karabayev, N., Baumuratova, D., & Benkhelifa, E. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02116-z · Cited by 1
Artificial Intelligence Technology in Computer Network Security
Lao, C., & Shen, Q. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-2092-1_73 · Cited by 1
In-Vehicle Intrusion Detection based on Machine Learning
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Niyato, D. T. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3150657
Dharsini, S., Prabhakar, G., Rajaram, S., & Shanthi, J. (2023) · International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI) · doi:10.1109/raeeucci57140.2023.10134411
Intrusion Detection Method Based on Network Performance Degradation
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Fang, X., Yao, X., Younas, W., & Bao, D. (2026) · International Journal of Communication Systems · doi:10.1002/dac.70405
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Qi, B., & Zhang, G. (2026) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70442
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Metadata
- Authors
- Talpur, A., & Gurusamy, M.
- Affiliations
- National University of Singapore (SG)
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2022
- Volume
- 24
- Issue
- 1
- Pages
- 346-379
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W3167949380
- Cited by
- 107
- References
- 220
- Citations by year
- 2026: 22 · 2025: 27 · 2024: 22 · 2023: 22 · 2022: 12
- FWCI
- 4.27
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- vehicular networks, VANET security, machine learning for security, attack detection, vehicular communication, security challenges, security requirements
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