A lightweight Anomaly based DDoS flood attack detection for Internet of vehicles

The concept of the Internet of Vehicles (IoV) enhances the VANETs by merging the VANETs with the Internet of things (IoT) thus making intelligent transportation systems a reality. The intelligent transport systems generate greater volumes of critical dynamic realtime data and thus raise a concern in the security of the generated data. The IoV has become a prominent field because of its scalability, reliable internet connection, and dynamic topological structures and due to its compatibility with various devices and sensors. IoV is susceptible to a range of attacks. The IoV consists of various kinds of components which involve various communications with sensors, vehicles, road infrastructure and humans. This paper will focus on UDP based Distributed Denial of Service (DDOS) Flood attacks. Onboard unit (OBU) is a computational device present in the vehicle is a resourceconstrained device a lightweight DDoS detection machine learning algorithm is required to detect the DDoS attack performed on the vehicles by a dataset generated using OMNET++ simulator