DARE: A Reports Dataset for Global Misbehavior Authority Evaluation in C-ITS

Abstract

European and North American governments are actively working on improving road safety and traffic efficiency. To this end, their corresponding standardization bodies: ETSI and IEEE are developing the Cooperative Intelligent Transport Systems (C-ITS). In this system, vehicles and road side units communicate in order to enable new services and propose cooperative safety applications. However, the system is vulnerable to new types of threats if not adequately secured. The security and privacy protection is crucial to the user acceptance of such new system. Currently, the ETSI and IEEE proposed using a specific vehicular Public Key Infrastructure (PKI) to protect the C-ITS system. The PKI can protect the system against external attackers but it still vulnerable to internal attacks. Registered vehicles with valid certificates can still disturb the system by misusing its applications. The aim of misbehavior detection is to detect and mitigate the effect of internal attackers. The current misbehavior detection architecture includes a local embedded component and a cloud component. In this paper, we propose a misbehavior reports dataset of derived from the local embedded detection of misbehaving entities. This dataset can be used to further develop and evaluate the cloud component. The set includes different road topology, varying attacker penetration rates and attack scenarios.