The DOI matches its Crossref record; 217 of the 220 references it cites were found in a registry or an index.
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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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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In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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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In the citing paper
“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
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Flagged first, then by citations
3 of the 220 references couldn’t be matched to a known paper.
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Items 101–108 of 108
Application of Quantum-AI Security in Intelligent Transportation
Rajkumar, K., Kumar, N. J., Tamilselvi, P., & Usharani, S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-7488-8.ch010
Computation offloading in the Internet of vehicles: use cases empowered by AI and blockchain
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Knowledge Discovery from Urban Trajectory Data: A Survey
da Silva, I. B. S. T. T., Bine, L. M. S., Borges, J. B., Viana, A. C., Ramos, H. S., & Loureiro, A. A. F. (2026) · ACM Computing Surveys · doi:10.1145/3833869
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Android Automotive OS under the hood: Architecture and security threat classification
Khan, F., Yu, H., Wu, J., Stakhanova, N., & Fung, B. C. M. (2026) · Computers & Security · doi:10.1016/j.cose.2026.105175
Ensuring Safety and Cybersecurity for Intelligent Vehicle Networks
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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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