TON-IoT: Detection of Attacks on Internet of Things in Vehicular Networks

During the recent era, due to the exponential rise in the Internet of things (IoT) enabled devices around the globe, IoT and Machine learning (ML) has come out as usable and efficient approach to supply productive solutions in this environment. Vehicular networks (VN) are considered to be the most important application domain where ML-based techniques are generated to address common attack issues and present insightful discussions. It is the union of smart transport and internet systems which is responsible for the passenger's safety and security as the attack threats have been growing very rapidly in VN. The main objective is to overwhelm the targeted IoT devices with malicious data traffic in VN. To solve the above-mentioned issues, this paper covers numerous types of attacks (backdoor, injection, Distributed Denial of service (DDoS), ransomware, password, scanning, Man in the middle (MITM) and cross-site scripting (XS S)) on VN deploying ML techniques using intrusion detection system (IDS) in the IoT based on the TON-IoT dataset. Due to the fact that it contains a variety of normal and malicious activities for various IoT services as well as heterogeneous data suppliers, TON-IoT provides a number of advantages that are completely missing from state-of-the-art datasets. The performance metrics are evaluated for the attack detection namely accuracy, precision, recall and F1-score using the three ML methods Random Forest (RF), Naive Bayes (NB) and K-Nearest Neighbour (KNN). Finally, the results conclude that amongst the three ML methods, KNN gives the highest accuracy rate i.e. 98.2%, while RF and NB give a 94% and 70% accuracy rate utilizing the ToN-IoT dataset.