A new procedure for misbehavior detection in vehicular ad-hoc networks using machine learning

Abstract

Misbehavior detection in vehicular ad hoc networks (VANETs) is performed to improve the traffic safety and driving accuracy. All the nodes in the VANETs communicate to each other through message logs. Malicious nodes in the VANETs can cause inevitable situation by sending message logs with tampered values. In this work, various machine learning algorithms are used to detect the primarily five types of attacks namely, constant attack, constant offset attack, random attack, random offset attack, and eventual attack. Firstly, each attack is detected by different machine learning algorithms using binary classification. Then, the new procedure is created to do the multi classification of the attacks on best chosen algorithm from different machine learning techniques. The highest accuracy in case of binary classification is obtained with Naïve Bayes (100%), decision tree (100%), and random forest (100%) in type1 attack, decision tree (100%) in type2 attack, and random forest (98.03%, 95.56%, and 95.55%) in Type4, Type8 and Type16 attack respectively. In case of new procedure for multi-classification, the highest accuracy is obtained with random forest (97.62%) technique. For this work, VeReMi dataset (a public repository for the malicious node detection in VANETs) is used.