Smooth is better than sharp: a random mobility model for simulation of wireless networks
MSWIM '01 Proceedings of the 4th ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems
Detecting and correcting malicious data in VANETs
Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks
Probabilistic validation of aggregated data in vehicular ad-hoc networks
Proceedings of the 3rd international workshop on Vehicular ad hoc networks
Secure and efficient key management in mobile ad hoc networks
Journal of Network and Computer Applications
Securing vehicular ad hoc networks
Journal of Computer Security - Special Issue on Security of Ad-hoc and Sensor Networks
Providing VANET security through active position detection
Computer Communications
A novel secure communication scheme in vehicular ad hoc networks
Computer Communications
Distributed misbehavior detection in VANETs
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Cluster-Based framework in vehicular ad-hoc networks
ADHOC-NOW'05 Proceedings of the 4th international conference on Ad-Hoc, Mobile, and Wireless Networks
Local Density Estimation and Dynamic Transmission-Range Assignment in Vehicular Ad Hoc Networks
IEEE Transactions on Intelligent Transportation Systems
Eviction of Misbehaving and Faulty Nodes in Vehicular Networks
IEEE Journal on Selected Areas in Communications
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Vehicular Ad Hoc Networks (VANETs) are appropriate networks that can be applied to intelligent transportation systems. In VANET, messages exchanged among vehicles may be damaged by attacker nodes. Therefore, security in message forwarding is an important factor. We propose the Detection of Malicious Vehicles (DMV) algorithm through monitoring to detect malicious nodes that drop or duplicate received packets and to isolate them from honest vehicles, where each vehicle is monitored by some of it trustier neighbors called verifier nodes. If a verifier vehicle observes an abnormal behavior from vehicle V, it increases distrust value of vehicle V. The ID of vehicle V is then reported to its relevant Certificate Authority (CA) as a malicious node when its distrust value is higher than a threshold value. Performance evaluation shows that DMV can detect most existence abnormal and malicious vehicles even at high speeds.