Message Authentication and Network Anomalies Detection in Vehicular Ad Hoc Networks

Abstract

Intelligent transport systems are the future in matters of safe roads and comfortable driving. Integration of vehicles into a unified intelligent network leads to all kinds of security issues and cyber threats common to conventional networks. Rapid development of mobile ad hoc networks and machine learning methods allows us to ensure security of intelligent transport systems. In this paper, we design an authentication scheme that can be used to ensure message integrity and preserve conditional privacy for the vehicle user. The proposed authentication scheme is designed with lightweight cryptography methods, so that it only brings little computational and communication overhead. We also conduct experiments on vehicular ad hoc network segment traffic generation in OMNeT++ tool and apply up-to-date machine learning methods to detect malicious behavior in a given simulated environment. The results of the study show high accuracy in distributed denial-of-service attack detection.