Spoofed data detection in VANETs using dynamic thresholds

Jonathan Petit, Michael Feiri, Frank Kargl

Abstract: Vehicular ad hoc networks aim at enhancing road safety by providing vehicle-to-vehicle communications and safety-related applications. But safety-related applications, like Local Danger Warning, need a high trust level in received messages. Indeed, decisions are made depending on these messages. To increase the trustworthiness, a consensus mechanism is used. Thus, vehicles make a decision when a threshold is reached. Setting this threshold is of main importance because it impacts the decision delay, and thus, the remaining time for a driver reaction. In this paper, we investigate the problem of threshold establishment without globally unique identifier system (GUID). We propose to model the threshold as a Kalman filter and provide an algorithm to dynamically update the threshold. By simulations, we investigate the problem of insider attackers that generate information forgery attacks. Simulation results show that our dynamic method suffers from a bootstrapping phase but reduces the percentage of wrong decisions. Nevertheless, as future work, further analysis of default threshold value will be done.