A Survey on Cyber-Security of Connected and Autonomous Vehicles (CAVs)
Abstract
As the general development trend of the automotive industry, connected and autonomous vehicles (CAVs) can be used to increase transportation safety, promote mobility choices, reduce user costs, and create new job opportunities. However, with the increasing level of connectivity and automation, malicious users are able to easily implement different kinds of attacks, which threaten the security of CAVs. Hence, this paper provides a comprehensive survey on the cyber-security in the environment of CAVs with the aim of highlighting security problems and challenges. Firstly, based on the types of communication networks and attack objects, it classifies various cyber-security risks and vulnerabilities in the environment of CAVs into in-vehicle network attacks, vehicle to everything network attacks, and other attacks. Next, it regards cyber-risk as another type of attacks in the environment of CAVs. Then, it describes and analyzes up-to-date corresponding defense strategies for securing CAVs. In addition, it concludes several available cyber-security and safety standards of CAVs, which is helpful for the practical application of CAVs. Finally, several challenges and open problems are discussed for the future research.
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?
Car-connected devices in ICVs can gather extensive information regarding the condition of vehicles and the privacy of drivers; if this data is accessed by hackers or malicious third parties, it can result in violation of personal privacy and potential misuse for illegal activities.
“Car-connected devices in ICVs can gather extensive information regarding the condition of vehicles and the privacy of drivers.If this data is accessed by hackers or malicious third parties, it can result in the violation of personal privacy and its potential misuse for illegal activities.”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [29]
Car-connected devices often communicate through wireless networks, and hackers can exploit these networks to infiltrate the vehicle's network system, steal data or manipulate instructions that control the vehicle, leading to unusual vehicle movement and compromised security.
“Additionally, car-connected devices often communicate with other devices or cloud services through wireless networks.Hackers can exploit these networks to infiltrate the vehicle's network system, thereby stealing data or manipulating instructions that control the vehicle, ultimately leading to unusual vehicle movement and compromising security [29].”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [29]
Previous research has identified common V2X communication failures, including crowded channels, malicious attacks, hardware failures, and frequency errors.
“Previous research has identified common V2X communication failures, including crowded channels, malicious attacks, hardware failures, and frequency errors [29,[79][80][81].”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [29]
As ICVs rely heavily on communication, a single security breach could compromise not only the personal data of passengers but also the functionality of the vehicle itself, leading to severe security risks.
“As ICVs rely heavily on communication, a single security breach could compromise not only the personal data of passengers but also the functionality of the vehicle itself, leading to severe security risks [29].”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [29]
The citation lists haven’t been fetched yet
References 132 · Cited by 370
- Authors
- Sun, X., Yu, F. R., & Zhang, P.
- Affiliations
- Shenzhen University (CN)
- Carleton University (CA)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2022-07
- Volume
- 23
- Issue
- 7
- Pages
- 6240-6259
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W3171142246
- Cited by
- 370
- References
- 132
- Citations by year
- 2026: 59 · 2025: 94 · 2024: 98 · 2023: 85 · 2022: 30 · 2021: 4
- FWCI
- 15.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- Funders
- National Natural Science Foundation of China (61802118)
- Science and Technology Innovation Projects of Shenzhen (JCYJ20190809152003992)
- China Postdoctoral Science Foundation (2019M653042)
- Government of Canada’s National Crime Prevention Strategy
- Natural Sciences and Engineering Research Council of Canada (NSERC) Collaborative Research and Training Experience (CREATE) Program for Building Trust in Connected and Autonomous Vehicles
- 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
- connected and autonomous vehicles, vehicle-to-everything communication, cybersecurity risks, security vulnerabilities, defense strategy, security standards, vehicle safety
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
