Intrusion Detection System Using Machine Learning for Vehicular Ad Hoc Networks Based on ToN-IoT Dataset

Abstract

Vehicular ad hoc networks (VANETs) are a subsystem of the proposed intelligent transportation system (ITS) that enables vehicles to communicate over the wireless communication infrastructure. VANETs are used in multiple applications, such as improving traffic safety and collision prevention. The use of VANETs makes the network vulnerable to various types of attacks, such as denial of service (DoS) and distributed denial of service (DDoS). Many researchers are now interested in adding a high level of security to VANETs. Machine learning (ML) methods were used for constructing a high level of security capabilities based on intrusion detection systems (IDSs). Furthermore, the vast majority of existing research is based on NSL-KDD or KDD-CUP99 datasets. Recent attacks are not present in these datasets. As a result, we employed a realistic dataset called ToN-IoT that derived from a large-scale, heterogeneous IoT network. This work tested various ML methods in both binary and multi-class classification problems. We used the Chi-square (Chi²) technique was used for feature selection and the Synthetic minority oversampling technique (SMOTE) for class balancing. According to the results, the XGBoost method outperformed other ML methods.