A Novel Recruitment Policy to Defend Against Sybils in Vehicular Crowdsourcing

Author

Federico Concone
Fabrizio De Vita
Ajay Pratap
Dario Bruneo
Giuseppe Lo Re
Sajal K. Das, Missouri University of Science and TechnologyFollow

Abstract

Vehicular Social Networks (VSNs) is an emerging communication paradigm, derived by merging the concepts of Online Social Networks (OSNs) and Vehicular Ad-hoc Networks (VANETs). Due to the lack of robust authentication mechanisms, social-based vehicular applications are vulnerable to numerous attacks including the generation of sybil entities in the networks. We address this important issue in vehicular crowdsourcing campaigns where sybils are usually employed to increase their influence and worsen the functioning of the system. In particular, we propose a novel User Recruitment Policy (URP) that, after extracting the participants within the event radius of a crowdsourcing campaign, detects and filters out the sybil vehicles by using a novel sybil detection approach, called SybilDriver. This technique combines the advantages of VANETs and OSNs by means of an innovative concept of proximity graph obtained from the physical vehicular network, in conjunction with a community detection and Random Forest techniques adopted in the OSN domain. Detailed experimental evaluations demonstrate the effectiveness of our approach and also show that it outperforms existing state-of-the-art methods typically used in the OSNs.

Recommended Citation

F. Concone et al., "A Novel Recruitment Policy to Defend Against Sybils in Vehicular Crowdsourcing," Proceedings of the 7th IEEE International Conference on Smart Computing (2021, Irvine, CA), pp. 105 - 112, Institute of Electrical and Electronics Engineers (IEEE), Aug 2021.

The definitive version is available at https://doi.org/10.1109/SMARTCOMP52413.2021.00035

Meeting Name

7th IEEE International Conference on Smart Computing, SMARTCOMP 2021 (2021: Aug. 23-27, Irvine, CA)

Department(s)

Computer Science

Comments

The work of S. K. Das was partially supported by NSF grants SaTC-2126619, CNS-1818942, DGE-1433659, OAC-1725755, and OAC-2104078.

Keywords and Phrases

Crowdsourcing; Proximity Graph; Sybil Detection; Trust and Truthfulness; Vehicular Social Network

International Standard Book Number (ISBN)

978-166541252-0

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2021 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

27 Aug 2021