IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
The sybil attack in sensor networks: analysis & defenses
Proceedings of the 3rd international symposium on Information processing in sensor networks
The Security and Privacy of Smart Vehicles
IEEE Security and Privacy
Detecting and correcting malicious data in VANETs
Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks
Detection and localization of sybil nodes in VANETs
DIWANS '06 Proceedings of the 2006 workshop on Dependability issues in wireless ad hoc networks and sensor networks
Self-certified Sybil-free pseudonyms
WiSec '08 Proceedings of the first ACM conference on Wireless network security
Securing vehicular ad hoc networks
Journal of Computer Security - Special Issue on Security of Ad-hoc and Sensor Networks
SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Authentication Mechanisms for Mobile Ad-Hoc Networks and Resistance to Sybil Attack
SECURWARE '08 Proceedings of the 2008 Second International Conference on Emerging Security Information, Systems and Technologies
A Sybil-Resistant Admission Control Coupling SybilGuard with Distributed Certification
WETICE '08 Proceedings of the 2008 IEEE 17th Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises
A Robust Detection of the Sybil Attack in Urban VANETs
ICDCSW '09 Proceedings of the 2009 29th IEEE International Conference on Distributed Computing Systems Workshops
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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.