Security and Privacy in V2X Communications: How Collaborative Learning can Improve Cybersecurity?

Pradip Sharma, Deepansu Vohra, Shailendra Rathore

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
97 Downloads (Pure)


Advances in cellular technology are a key driver of the growing automotive Vehicle to Everything (V2X) market. In V2X communications, information from sensors and other sources travels via high-bandwidth, low-latency, high-reliability links, paving the way to fully autonomous driving and intelligent mobility. With the future adoption of 5G and beyond (5G&B) networks, V2X is likely to generate a huge volume of data, which encourages the use of edge computing and pushes the system to learn the model locally to support real-time applications. However, the edge computing paradigm raises concerns about the security and privacy of local nodes (e.g., vehicles) and the increased risk of cyberattacks. In this article, we identify open research questions, key requirements, and potential solutions to provide cyber resilience in V2X communications.
Original languageEnglish
Pages (from-to)32-39
Number of pages8
JournalIEEE Network
Issue number3
Early online date13 Jul 2022
Publication statusPublished - 13 Jul 2022

Bibliographical note

We thank Professor Nir Oren (Department of Computing Science, University of Aberdeen, UK) and Professor Steven Furnell (University of Nottingham, UK) for their expertise and assistance throughout all aspects of our study and for his contribution to the technical review and proofreading the article.


  • V2X
  • Autonomous Vehicles
  • 5G&B Networks
  • Cybersecurity
  • Collaborative Learning
  • Privacy
  • Solid modeling
  • Collaborative work
  • Data models
  • Vehicular ad hoc networks
  • Vehicle-to-everything
  • Edge computing


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