A Classification of Misbehavior Detection Schemes for VANETs: A Survey

In today’s era, thinking of Vehicular Ad-hoc Network as a midrib for the leaf academic, social, corporate, and economic activities will not be erroneous. To avoid any panic situations like road accidents, heavy traffic jams, etc., timely availability correct information is obligatory. The presence malicious nodes within network ruin dream establishing safe, secure, accident-free vehicular network. This objective can fulfilled only when are identified correctly, respective actions taken at right time. Therefore, there great requirement efficient intelligent misbehavior detection techniques to deal with such situations. networks very prone numerous attacks, Sybil unauthorized access, etc. due their dynamic nature. main goal this study discuss bundle various available schemes solutions cope harmful attackers in We have categorized different detections on criteria architecture, approach, node-centric, data-centric. subcategorization also given paper. One section paper focuses role machine learning an emerging foot strap further enhancement. A comparative analysis conducted based performance measures accuracy, False Positive Rate, Recall, Precision F-measurement. Finally, concluded by discussing open issues research challenges associated Network.