Detecting Sybil Attacks in VANET: Exploring Feature Diversity and Deep Learning Algorithms with Insights into Sybil Node Associations-Reference-Cited by-同舟云学术

Detecting Sybil Attacks in VANET: Exploring Feature Diversity and Deep Learning Algorithms with Insights into Sybil Node Associations

Published:2024-05-23 Issue:3 Volume:32 Page:
ISSN:1064-7570
Container-title:Journal of Network and Systems Management
language:en
Short-container-title:J Netw Syst Manage

Author:

Sultana Rukhsar,Grover Jyoti,Tripathi Meenakshi,Sachdev Manhar Singh,Taneja Sparsh

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10922-024-09827-7.pdf

Reference43 articles.

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3. ITS.: EN 302 637-2—V1.3.2—Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications; Part 2: Specification of Cooperative Awareness Basic Service. ETSI WG5 Technical Specification (2014)

4. Committee, D., et al.: SAE J2735 Dedicated Short Range Communications (DSRC) Message Set Dictionary. SAE Std, 2735–201 (2015)

5. Mousavinejad, E., Vlacic, L.: Secure platooning control of automated vehicles under cyber attacks. ISA Trans. 127, 229–238 (2022)

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1. Decentralized Distance-based Strategy for Detection of Sybil Attackers and Sybil Nodes in VANET;Journal of Network and Systems Management;2024-09-10

2. Machine Learning for Intrusion Detection in Vehicular Ad-hoc Networks (VANETs): A Survey;2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA);2024-08-06