This chapter describes how Vehicular Ad hoc Networks (VANETs) are classes of ad hoc networks that provides communication among various vehicles and roadside units. VANETs being decentralized are susceptible to many security attacks. A flooding attack is one of the major security threats to the VANET environment. This chapter proposes a hybrid Intrusion Detection System which improves accuracy and other performance metrics using Artificial Neural Networks as a classification engine and a genetic algorithm as an optimization engine for feature subset selection. These performance metrics have been calculated in two scenarios, namely misuse and anomaly. Various performance metrics are calculated and compared with other researchers' work. The results obtained indicate a high accuracy and precision and negligible false alarm rate. These performance metrics are used to evaluate the intrusion system and compare with other existing algorithms. The classifier works well for multiple malicious nodes. Apart from machine learning techniques, the effect of the network parameters like throughput and packet delivery ratio is observed.
Intrusion Detection System Using Machine Learning for Vehicular Ad Hoc Networks Based on ToN-IoT Dataset