Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Securing vehicular ad hoc networks
Journal of Computer Security - Special Issue on Security of Ad-hoc and Sensor Networks
Distributed misbehavior detection in VANETs
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
A novel defense mechanism against sybil attacks in VANET
Proceedings of the 3rd international conference on Security of information and networks
VANET alert endorsement using multi-source filters
Proceedings of the seventh ACM international workshop on VehiculAr InterNETworking
Eviction of Misbehaving and Faulty Nodes in Vehicular Networks
IEEE Journal on Selected Areas in Communications
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Detection of misbehaviors in Vehicular Ad Hoc Networks (VANETs) using machine learning methods has not been investigated extensively. In VANET, an illegitimate vehicle may transmit inaccurate messages to trigger an un- avoidable situation. In this paper, we present an ensemble based machine learning approach to classify misbehaviors in VANET. The performance of classifiers used for classification depends on the induction algorithms. We exploit the strengths of different classifiers using an ensemble method that combines the results of individual classifiers into one final result in order to achieve higher detection accuracy. Proposed security framework to classify different types of misbehaviors is implemented using WEKA. Features of nodes participating in VANET are extracted by performing experiments in NCTUns-5.0 simulator with different simulation scenarios (varying the number of legitimate and misbehaving nodes). We evaluate ensemble method using five different base inducers (Naive Bayes, IBK, RF, J48, Adaboost(J48)). We also show that ensemble based approach is more efficient in classifying multiple misbehaviors present in VANET as compared to base classifiers used for classification.