Shielding the Connected Cars: A Dataset-Powered Defense Against DDoS

The connected car is no longer a theoretical concept and is now in the application phase. This puts a burden on the infrastructure of the connected cars since it is at the heart of this technology. The research is now focused on how to protect this infrastructure against the well-known serious attacks, among which is the Distributed Denial of Service (DDoS) attack. The messages exchanged between the cars and the infrastructure are secured by digital certificates, this protects the network from external attacks only. Therefore, there is a need for an extra layer of security that protects the system from insider attacks in the form of an Intrusion Detection System (IDS). While researchers have made efforts to simulate such an attack in a near-realistic setup, the work still lacks its true representation. To contribute to this research gap, we present in this paper the generation of a publicly available dataset representing a DDoS attack simulated using Cooperative Awareness Messages in a well-known vehicular traffic simulation to be used to develop an IDS. The data collection is done on the Road Side Unit reflecting real data collection. Also, we propose an ensemble learning-driven approach as a potential framework for creating connected cars Vehicle-to-Everything IDS. Where we delve into the propositions of its deployment and the architecture of it. The obtained results show a Matthews correlation coefficient of 98% by the proposed models countering the attacks.