A Fake Message and Malicious Node Detection Method Using Machine Learning in V-NDN

Abstract

V-NDN (Vehicular Named Data Networking) applies completely new communication mechanism to the Internet of Vehicles, which can effectively improve the efficiency of vehicle communication and reduce communication overhead. However, the dynamic characteristics of the vehicle network topology cause malicious nodes to join the network and send fake messages to normal nodes, which brings many security challenges to the reliable communication between vehicles in the V-NDN. In this paper, we propose a method based on machine learning to detect fake messages and malicious nodes. The proposed method simplifies the detection process into a classification process and combines with a variety of machine learning algorithms such as logistic regression, KNN. It can intelligently identify fake messages and malicious nodes in the V-NDN. The simulation results show that the method can effectively increase the accuracy and efficiency of identifying fake messages and malicious nodes and improve the security in the V-NDN.