Paper 2022/1700

Comparative Study of HDL algorithms for Intrusion Detection System in Internet of Vehicles

Abstract

Internet of vehicles (IoV) has brought technological revolution in the fields of intelligent transport system and smart cities. With the rise in self-driven cars and AI managed traffic system, threats to such systems have increased significantly. There is an immediate need to mitigate such attacks and ensure security, trust and privacy. Any malfunctioning or misbehaviour in an IoV based system can lead to fatal accidents. This is because IoV based systems are sensitive in nature involving human lives either on or off the roads. Any compromise to such systems can affect user safety and incur in service delays. For IoV users, the Intrusion Detection System (IDS) is crucial to protect them from different malware-based attacks and to ensure the security of users and infrastructures. Machine Learning approaches are used for extracting useful features from network traffic and also for predicting the patterns of anomalous activities. We use two datasets, namely Balanced DDoS dataset and Car-Hacking Dataset for comparative study of intrusion detection using various machine learning approaches. The comparative study shows the differences of various machine learning and deep learning approaches against two datasets.