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Protecting Mobile Crowd Sensing against Sybil Attacks Using Cloud Based Trust Management System

Mobile Information Systems. 2016;2016 DOI 10.1155/2016/6506341

 

Journal Homepage

Journal Title: Mobile Information Systems

ISSN: 1574-017X (Print); 1875-905X (Online)

Publisher: Hindawi Limited

LCC Subject Category: Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS


Shih-Hao Chang (Department of Computer Science and Information Engineering, Tamkang University, New Taipei City 25137, Taiwan)

Zhi-Rong Chen (Department of Computer Science and Information Engineering, Tamkang University, New Taipei City 25137, Taiwan)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 48 weeks

 

Abstract | Full Text

Mobile crowd sensing (MCS) arises as a new sensing paradigm, which leverages citizens for large-scale sensing by various mobile devices to efficiently collect and share local information. Unlike other MCS application challenges that consider user privacy and data trustworthiness, this study focuses on the network trustworthiness problem, namely, Sybil attacks in MCS network. The Sybil attack in computer security is a type of security attack, which illegally forges multiple identities in peer-to-peer networks, namely, Sybil identities. These Sybil identities will falsify multiple identities that negatively influence the effectiveness of sensing data in this MCS network or degrading entire network performance. To cope with this problem, a cloud based trust management scheme (CbTMS) was proposed to detect Sybil attacks in the MCS network. The CbTMS was proffered for performing active and passive checking scheme, in addition to the mobile PCS trustworthiness management, and includes a decision tree algorithm, to verify the covered nodes in the MCS network. Simulation studies show that our CbTMS can efficiently detect the malicious Sybil nodes in the network and cause 6.87 Wh power reduction compared with other malicious Sybil node attack mode.