Anomalous Data Detection in Vehicular Networks Using Traffic Flow Theory

The world is embracing the presence of connected autonomous vehicles which are expected to play a major role in the future of intelligent transport systems. Given such connectivity, vehicles in the networks are vulnerable to making incorrect decisions due to anomalous data. No sophisticated attacks are required; just a vehicle reporting anomalous speeds would be sufficient to disrupt the entire traffic flow. Detection of such anomalies is vital for a secured vehicular network. Nevertheless, the attention given for the use of physics of traffic flow to secure vehicular networks is relatively less. We propose to integrate traffic flow phenomena within anomalous data detection techniques to improve the evaluation of threats in vehicular networks. We apply traffic flow theory under steady state assumptions to identify anomalous data. The numerical results indicate the proposed method to provide reliable and consistent predictions.