Security and privacy in V2X communications: how can collaborative learning improve cybersecurity?

Pradip Kumar Sharma, Deepansu Vohra, Shailendra Rathore

Research output: Contribution to journalArticlepeer-review

6 Downloads (Pure)

Abstract

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
Volume36
Issue number3
Early online date13 Jul 2022
DOIs
Publication statusPublished - 13 Jul 2022

Keywords

  • V2X
  • Autonomous vehicles
  • 5G&B networks
  • Cybersecurity
  • Collaborative learning

Fingerprint

Dive into the research topics of 'Security and privacy in V2X communications: how can collaborative learning improve cybersecurity?'. Together they form a unique fingerprint.

Cite this