Detecting and correcting malicious data in VANETs
Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks
The security of vehicular ad hoc networks
Proceedings of the 3rd ACM workshop on Security of ad hoc and sensor networks
Spatio-temporal variations of vehicle traffic in VANETs: facts and implications
Proceedings of the sixth ACM international workshop on VehiculAr InterNETworking
TACKing together efficient authentication, revocation, and privacy in VANETs
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
Distributed misbehavior detection in VANETs
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
On trust models and trust evaluation metrics for ad hoc networks
IEEE Journal on Selected Areas in Communications
Efficient and secure threshold-based event validation for VANETs
Proceedings of the fourth ACM conference on Wireless network security
Misbehavior detection based on ensemble learning in VANET
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
Security challenges for the intelligent transportation system
Proceedings of the First International Conference on Security of Internet of Things
Review: Information management in vehicular ad hoc networks: A review
Journal of Network and Computer Applications
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We propose a security model for Vehicular Ad-hoc Networks (VANETs) to distinguish spurious messages from legitimate messages. In this paper, we explore the information available in a VANET environment to enable vehicles to filter out malicious messages which are transmitted by a minority of misbehaving vehicles. More specifically, we introduce a message filtering model that leverages multiple complementary sources of information to construct a multi-source detection model such that drivers are only alerted after some fraction of sources agree. Our filtering model is based on two main components: a threshold curve and a Certainty of Event (CoE) curve. A threshold curve implies the importance of an event to a driver according to the relative position, and a CoE curve represents the confidence level of the received messages. An alert is triggered when the event certainty surpasses a threshold. We analyze our model and provide some initial simulation results to demonstrate the benefits.