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Accepted VersionCC BY-SA V4.0, Open Access
Preprint
Security, Privacy and Trust: Cognitive Internet of Vehicles
arXiv, Vol.26 April 2021
Cornell University
2021
Abstract
The recent advancement of cloud technology offers unparallel strength to support intelligent computations and advanced services to assist with automated decisions to improve road transportation safety and comfort. Besides, the rise of machine intelligence propels the technological evolution of transportation systems one step further and leads to a new framework known as Cognitive Internet of Vehicles (C-IoV). The redefined cognitive technology in this framework promises significant enhancements and optimized network capacities compared with its predecessor framework, the Internet of Vehicles (IoV). CIoV offers additional security measures and introduces security and privacy concerns, such as evasion attacks, additional threats of data poisoning, and learning errors, which may likely lead to system failure and road user fatalities. Similar to many other public enterprise systems, transportation has a significant impact on the population. Therefore, it is crucial to understand the evolution and equally essential to identify potential security
vulnerabilities and issues to offer mitigation towards success. This chapter offers discussions framing answers to the following two questions, 1) how and in what ways the penetration of the latest technologies are reshaping the transportation system? 2) whether the evolved system is capable of addressing the concerns of cybersecurity? This chapter, therefore, starts presenting the evolution of the transportation system followed by a quick overview of the evolved CIoV, highlighting the evolved cognitive design. Later it presents how a cognitive engine can overcome legacy security concerns and also be subjected to further potential security, privacy, and trust issues that this cloud-based evolved transportation system may encounter.
Details
- Title
- Security, Privacy and Trust: Cognitive Internet of Vehicles
- Authors
- Khondokar Fida Hasan (Author) - Queensland University of TechnologyAnthony Overall (Author) - Queensland University of TechnologyKeyvan Ansari (Author) - University of the Sunshine Coast, QueenslandGowri Ramachandran (Author) - Queensland University of TechnologyRaja Jurdak (Author) - Queensland University of Technology
- Publication details
- arXiv, Vol.26 April 2021
- Publisher
- Cornell University
- Date published
- 2021
- DOI
- 10.48550/arXiv.2104.12878
- ISSN
- 2331-8422
- Copyright note
- Copyright (c) 2021. This work is licenced under this a Creative Commons Attribution Share Alike 4.0 licence. Details: https://creativecommons.org/licenses/by-sa/4.0/
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99642370702621
- Output Type
- Preprint
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