Towards the Safety of Intelligent Transportation: A Survey on the Security Challenges and Mitigations in Internet of Vehicles (IoV)
Abstract
The Internet of Vehicles (IoV) marks a revolutionary leap in transportation, integrating vehicles with the Internet to enhance convenience, intelligence, and efficiency. As IoV applications continue to expand, a spectrum of challenges and untapped opportunities emerge. Among these, the security of IoV stands out as a critical issue in transportation systems, given its direct impact on road safety. To this end, this survey conducts an in-depth analysis of IoV security challenges and introduces a distinctive approach by systematically categorizing threats into inside-vehicle and outside-vehicle domains, providing a comprehensive understanding of the full spectrum of risks. Meanwhile, we examine defense mechanisms designed to counter these threats and explore proactive strategies to enhance IoV security. In addition, this study explores emerging protection techniques, such as AI-driven intrusion detection, blockchain-based trust management, and 5G-enabled secure routing, demonstrating how these technologies can be effectively integrated to safeguard IoV systems. Furthermore, this survey provides actionable recommendations and forward-looking research directions to guide the development of real-world implementations, standardized procedures, and regulatory frameworks, benefiting stakeholders, policymakers, and researchers. As IoV continues its rapid evolution, these insights offer a comprehensive roadmap to strengthen IoV security and contribute to a safer, more resilient intelligent transportation ecosystem.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
IoV security has been reviewed by Maoudi et al. [102] and Junejo et al. [103] without an LLM agent lens.
“IoV security has been reviewed by Maoudi et al. [102] and Junejo et al. [103] without an LLM agent lens.”
From Agentic AI and Large Language Models for Autonomous IoT Cybersecurity: A Systematic Survey, Taxonomy, and Research Roadmap (2026) · cites this paper as [102]
Cited by
Sorted by citations
Nageshwaran, V., & Ezekiel, S. (2026) · Electronics · doi:10.3390/electronics15122740 · Cited by 4 · see citing works
“IoV security has been reviewed by Maoudi et al. [102] and Junejo et al. [103] without an LLM agent lens.”
- Authors
- Das, R., Zhou, L., Bi, S., Wang, T., & Hou, T.
- Affiliations
- Texas State University (US)
- University of North Texas (US)
- Venue
- ACM Transactions on Sensor Networks
- Published
- 2026-04-13
- Volume
- 22
- Issue
- 3
- Pages
- 1-44
- ISSN
- 1550-4859 (ISSN-L)
- 1550-4867
- OpenAlex
- W7117573592
- Cited by
- 7
- References
- 0
- Citations by year
- 2026: 7
- FWCI
- 1.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Sensor Networks (publishedVersion)
- Funders
- Army Research Office (W911NF-24-1-0088)
- 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 › Engineering › Mechanical Engineering › IoT and GPS-based Vehicle Safety Systems
- Keywords
- Internet of Vehicles, intelligent transportation systems, road safety, threat categorization, intrusion detection, security standards and regulations
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review