An Edge-enabled Virtual Honeypot Based Intrusion Detection System for Vehicle-to-Everything (V2X) Security using Machine Learning.

Securing vehicle-to-everything (V2X) communications is essential as intelligent transportation system integration progresses to guarantee the dependability and safety of connected vehicles. Our study presents a novel approach aimed at strengthening the security of vehicles in V2X networks. The proposed system utilizes the virtual honeypots technique, referred to as PotRSU, within roadside units (RSU) to gather data from heterogeneous sources. The malicious entities that are drawn from all incoming traffic are recorded by the PotRSU. We utilized machine learning algorithms to effectively identify intrusion. The analysis and experimentation conducted on the proposed system exhibit 99.01% accuracy in identifying malicious nodes. [ABSTRACT FROM AUTHOR]