Towards the Development of Realistic DoS Dataset for Intelligent Transportation Systems

Vehicular ad-hoc networks (VANETs) present security vulnerabilities, which make them prone to diverse cyberattacks. Denial of Service (DoS) is one of the most prevalent and severe cyberattack that targets VANETs. To tackle this cyberattack and mitigate its effect, intrusion detection systems need to be developed. To this end, a realistic and representative dataset is essential to train and validate the systems. This paper proposes a new dataset, VDoS-LRS, which includes legitimate and simulated vehicular network traffic, along with different types of DoS cyberattack. We also present a realistic testbed environment instead of simulators, taking into consideration different environments (urban, highway and rural). In addition, we explore a wide range of traffic features for detecting and classifying vehicular traffic. We evaluate the reliability of the VDoS-LRS dataset using different machine learning algorithms for forensics purposes. The experimental results showed that it is possible to detect effectively different types of DoS cyberattack within diverse environments.