Sybil attack detection based on signature vectors in VANETs

Sybil attack is one of the serious threats in vehicular ad hoc networks (VANETs) because drivers may receive wrong information, which could lead to injury the lives of the drivers and passengers, when they are under Sybil attack. This paper, therefore, presents a novel solution named Sybil attack detection based on signature vectors (SADSIV) in VANETs. Each node gathers the digital signatures in their moving; then our algorithm detects Sybil attack by analysing and comparing vehicle nodes' signature vectors independently under the condition of inadequate infrastructures. We improve the feasibility of our approach through the limited infrastructures at the early deployment stages of VANETs. In addition, the independency and feasibility of our algorithm are more robust than the existing solutions which rely on collaboration of neighbouring nodes. Simulation results show that our method outperforms the existing detection schemes in terms of robustness, detection rate and lower system requirements.

[1]  Valérie Viet Triem Tong,et al.  A Sybil-Resistant Admission Control Coupling SybilGuard with Distributed Certification , 2008, 2008 IEEE 17th Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises.

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

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

[4]  Markulf Kohlweiss,et al.  Self-certified Sybil-free pseudonyms , 2008, WiSec '08.

[5]  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.

[6]  S. Kay Fundamentals of statistical signal processing: estimation theory , 1993 .

[7]  John R. Douceur,et al.  The Sybil Attack , 2002, IPTPS.

[8]  Maxim Raya,et al.  Securing vehicular ad hoc networks , 2007, J. Comput. Secur..

[9]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[10]  George Danezis,et al.  SybilInfer: Detecting Sybil Nodes using Social Networks , 2009, NDSS.

[11]  Feng Xiao,et al.  SybilLimit: A Near-Optimal Social Network Defense Against Sybil Attacks , 2010, IEEE/ACM Trans. Netw..

[12]  Kaixin Xu,et al.  Group and swarm mobility models for ad hoc network scenarios using virtual tracks , 2004, IEEE MILCOM 2004. Military Communications Conference, 2004..

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

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

[15]  John M. Brooke,et al.  Authentication Mechanisms for Mobile Ad-Hoc Networks and Resistance to Sybil Attack , 2008, 2008 Second International Conference on Emerging Security Information, Systems and Technologies.

[16]  Adrian Perrig,et al.  Challenges in Securing Vehicular Networks , 2005 .

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

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