Paper Info
Title
A Traffic Flow Theory Aided Physical Measurement-Based Sybil Nodes Detection Mechanism in Vehicular Ad-hoc Networks
Abstract
In traffic safety related application of Vehicular Ad-hoc Networks (VANETs), security is a great important issue. Sybil attack is a particular kind of attack where the attacker illegitimately claims multiple identities. In the past years, several approaches have been proposed for solving this problem. They are categorized into PKI-based, infrastructure-based, observer-based, and resource-test-based schemes. In this paper, previous protocols are analyzed, and a novel scheme to detect the Sybil nodes in VANETs is presented, mitigating the effect of a Sybil attack. The proposed Sybil nodes detection scheme, Traffic Flow Theory Aided Physical Measurement-Based Sybil Nodes Detection Mechanism in VANETs (PMSD), takes advantage of unmodifiable physical measurements of the beacon messages instead of key-based materials, which dose not only solve the Sybil attack problem, but also reduces the overhead for the detection. The proposed scheme does not require fixed infrastructure, which makes it easy to implement. To increase the detection accuracy, traffic flow theory and safety guard distance is introduced into the scheme. The simulation results show a 97% detection rate of Sybil nodes, with only about a 2% error rate.
Year
DOI
Venue
2014
10.1109/ICIS.2014.6912147
ICIS
Keywords
DocType
Volume
vanet,observer-based schemes,key-based materials,security,safety guard distance,physical measurement,traffic safety related application,resource-test-based schemes,sybil nodes detection scheme,pki-based schemes,infrastructure-based schemes,traffic flow theory,vehicular ad hoc networks,physical measurement-based sybil nodes detection mechanism,vehicular ad-hoc networks,pmsd,vanets,telecommunication traffic,beacon messages,unmodifiable physical measurements,sybil attack,security of data,synchronization,sensors
Conference
3
Issue
ISSN
Citations 
2
2211-7938
3
PageRank 
References 
Authors
0.43
3
2
Name
Order
Citations
PageRank
Dongxu Jin1173.08
JooSeok Song230658.82