Multivariate verification for sybil attack detection in VANET

Abstract Wireless vehicular communication is used to enhance traffic safety and to minimize congestion, thereby leading to increased driving efficiency. A malicious node can transmit an inaccurate message to trigger inevitable situations by pretending to be multiple (other) vehicles. Therefore, it is critical to identify malicious nodes as well as fake messages generated by such nodes, and discard such messages quickly. In a Sybil attack, an attacker participates in the network with multiple forged identities in order to disrupt the fundamental operations of VANET. Sybil attacks are particularly easy to launch in VANETs due to the open and broadcast nature of communication medium. In this paper, we present the implementation of simulated Sybil attack scenario in VANET and its consequences on the performance of the network. We also propose a lightweight, scalable and distributed detection approach based on the difference in movement patterns of Sybil nodes and legitimate nodes. In our approach, each Road Side Unit (RSU) computes, stores and verifies various parameter values including RSS, distance, angle of passing-by vehicles through passive overhearing process to detect Sybil attackers. The combination of different parameters makes our detection approach highly accurate. We validate our results on realistic traces obtained from a multi-agent microscopic traffic simulator (MMTS). Simulation results show the effectiveness of the proposed approach to locate Sybil nodes with a different number of network parameters.

[1]  François Spies,et al.  Impact of radio propagation models in vehicular ad hoc networks simulations , 2006, VANET '06.

[2]  Brian Neil Levine,et al.  Detecting the Sybil Attack in Mobile Ad hoc Networks , 2006, 2006 Securecomm and Workshops.

[3]  Xin Wang,et al.  A Robust Detection of the Sybil Attack in Urban VANETs , 2009, 2009 29th IEEE International Conference on Distributed Computing Systems Workshops.

[4]  Xiaodong Wang,et al.  Detecting the Sybil Attack Cooperatively in Wireless Sensor Networks , 2008, 2008 International Conference on Computational Intelligence and Security.

[5]  Thomas R. Gross,et al.  An evaluation of inter-vehicle ad hoc networks based on realistic vehicular traces , 2006, MobiHoc '06.

[6]  Bin Xiao,et al.  Detection and localization of sybil nodes in VANETs , 2006, DIWANS '06.

[7]  Wen-Chung Chang,et al.  Detecting Sybil attacks in Wireless Sensor Networks using neighboring information , 2009, Comput. Networks.

[8]  Sarit Pal,et al.  Defending Mechanisms Against Sybil Attack in Next Generation Mobile Ad Hoc Networks , 2008 .

[9]  Damla Turgut,et al.  Defense against Sybil attack in vehicular ad hoc network based on roadside unit support , 2009, MILCOM 2009 - 2009 IEEE Military Communications Conference.

[10]  George Kesidis,et al.  Robust Sybil Detection for MANETs , 2009, 2009 Proceedings of 18th International Conference on Computer Communications and Networks.

[11]  Bertrand Ducourthial,et al.  Sybil Nodes Detection Based on Received Signal Strength Variations within VANET , 2009, Int. J. Netw. Secur..

[12]  Peng Ning,et al.  Privacy-Preserving Detection of Sybil Attacks in Vehicular Ad Hoc Networks , 2007, 2007 Fourth Annual International Conference on Mobile and Ubiquitous Systems: Networking & Services (MobiQuitous).

[13]  Huirong Fu,et al.  Verifying position and velocity for vehicular ad-hoc networks , 2011, Secur. Commun. Networks.

[14]  Fan Bai,et al.  Towards Characterising and Classifying Communication–based Automotive Applications from a Wireless Networking Perspective , 2012 .

[15]  Zhiyi Fang,et al.  Securing Vehicular Ad Hoc Networks , 2007, 2007 2nd International Conference on Pervasive Computing and Applications.

[16]  Brian Neil Levine,et al.  A Survey of Solutions to the Sybil Attack , 2006 .

[17]  Jessica Staddon,et al.  Detecting and correcting malicious data in VANETs , 2004, VANET '04.

[18]  Luca Delgrossi,et al.  IEEE 802.11p: Towards an International Standard for Wireless Access in Vehicular Environments , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[19]  Ciarán Bryce,et al.  Using TPMs to Secure Vehicular Ad-Hoc Networks (VANETs) , 2008, WISTP.

[20]  SsuKuo-Feng,et al.  Detecting Sybil attacks in Wireless Sensor Networks using neighboring information , 2009 .

[21]  Xin Wang,et al.  Sybil attack detection based on signature vectors in VANETs , 2011, Int. J. Crit. Comput. Based Syst..

[22]  Murat Demirbas,et al.  An RSSI-based scheme for sybil attack detection in wireless sensor networks , 2006, 2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks(WoWMoM'06).

[23]  Srdjan Capkun,et al.  The security and privacy of smart vehicles , 2004, IEEE Security & Privacy Magazine.

[24]  Vijay Laxmi,et al.  Performance Evaluation and Detection of Sybil Attacks in Vehicular Ad-Hoc Networks , 2010, CNSA.

[25]  Bertrand Ducourthial,et al.  On the Sybil attack detection in VANET , 2007, 2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems.

[26]  Heekuck Oh,et al.  On Secure and Privacy-Aware Sybil Attack Detection in Vehicular Communications , 2014, Wirel. Pers. Commun..

[27]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[28]  Gongjun Yan,et al.  Providing VANET security through active position detection , 2007, VANET '07.

[29]  Wei Yang,et al.  Method of Detecting the Sybil Attack Based on Ranging in Wireless Sensor Network , 2009 .

[30]  Vijay Laxmi,et al.  A novel defense mechanism against sybil attacks in VANET , 2010, SIN.

[31]  A. Perrig,et al.  The Sybil attack in sensor networks: analysis & defenses , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[32]  Wei Yang,et al.  Method of Detecting the Sybil Attack Based on Ranging in Wireless Sensor Network , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.