Paper Info
Title
Power Control Identification: A Novel Sybil Attack Detection Scheme in VANETs Using RSSI
Abstract
Vehicular <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</italic> networks (VANETs) have far-reaching application potentials in the intelligent transportation system (ITS) such as traffic management, accident avoidance and in-car infotainment. However, security has always been a challenge to VANETs, which may cause severe harm to the ITS. Sybil attack is considered as a serious security threat to VANETs since the adversary can disseminate false messages with multiple forged identities to attack various applications in the ITS. RSSI-based Sybil nodes detection is an efficient scheme against Sybil attacks, which adopts position estimation, distribution verification or similarity comparison to identify Sybil nodes. However, when Sybil nodes conduct power control to deliberately change transmission powers, the received RSSI values would change correspondingly, which leads to inaccurate localization or different RSSI time series of these Sybil nodes. Thus, it is very difficult to differentiate Sybil nodes from normal nodes via conventional RSSI-based methods. This paper first discusses potential power control models (PCMs) for launching Sybil attacks in VANETs, then presents two simple Sybil attack models and three sophisticated Sybil attack ones with or without power control in detail, finally proposes a power control identification Sybil attack detection (PCISAD) scheme to find anomalous variations in RSSI time series, which are then used to identify Sybil nodes via a linear SVM classifier. Extensive simulations and real-world experiments prove that the proposed scheme can effectively deal with Sybil attacks with power control.
Year
DOI
Venue
2019
10.1109/JSAC.2019.2933888
IEEE Journal on Selected Areas in Communications
Keywords
Field
DocType
Power control,Peer-to-peer computing,Time series analysis,Security,Estimation,Data models,Phase change materials
Data modeling,Computer science,Power control,Computer network,Sybil attack,Dissemination,Intelligent transportation system,Wireless ad hoc network,Classifier (linguistics),Accident avoidance
Journal
Volume
Issue
ISSN
37
11
0733-8716
Citations 
PageRank 
References 
3
0.38
0
Authors
6
Name
Order
Citations
PageRank
Yuan Yao162.81
Bin Xiao21763129.31
Gang Yang341.77
Yujiao Hu462.47
Liang Wang51567158.46
Xingshe Zhou6283.80