Enhancing Vehicular Network Security: An In-Depth analysis of Machine Learning Approaches

Clicks: 1
ID: 312830
2024
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #483 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Modern transportation systems heavily rely on vehicular networks, facilitating crucial applications such as autonomous driving, in-car infotainment, traffic management, speed restriction, and road safety. These networks primarily utilize the Vehicular Ad Hoc Network (VANET) architecture, which connects vehicles via roadside units (RSUs) to the edge network and ultimately to a backbone network through wired or wireless connections. However, the open and dynamic nature of VANETs introduces various security challenges that can compromise vehicular communications, potentially jeopardizing the safety and efficiency of intelligent transportation systems. This study examines the current state of security services, common attacks, and application scenarios specific to VANETs, with a focus on machine learning techniques to strengthen these networks. It evaluates advancements, identifies gaps, and suggests future research directions to enhance the robustness and resilience of VANETs in an increasingly connected and automated transportation environment. This study aims to support ongoing efforts to address security issues in VANETs and enable the full potential of vehicular networks in future transportation systems.
Reference Key
imported_1777057163_69ebbd8bd6620 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Syed Aleem Muzaffar
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/784
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.