Towards Efficient Certificate Revocation Status Validation in Vehicular Ad Hoc Networks with Data Mining

Abstract

Vehicular Ad hoc Networks (VANETs) are emerging as a promising approach to improving traffic safety and providing a wide range of wireless applications for drivers and passengers. To perform reliable and trusted vehicular communications, one prerequisite is to ensure a peer vehicle’s credibility by means of digital certificates validation from messages that are sent out by other vehicles. However, in vehicular communication systems, certificates validation is more time consuming than in traditional networks, due to the fact that each vehicle receives a large number of messages in a short period of time. Another issue that needs to be addressed is the unsuccessful delivery of information between vehicles and other entities on the road as a result of their high mobility rate. For these reasons, we need new solutions to accelerate the process of certificates validation. In this thesis, we propose a certificate revocation status validation scheme using the concept of clustering; based on data mining practices, which can meet the aforementioned requirements. We employ the technique of k -means clustering to boost the efficiency of certificates validation, thereby enhancing the security of a vehicular ad hoc network. Additionally, a comprehensive analysis of the security of the proposed scheme is presented. The analytical results demonstrate that this scheme can effectively improve the validation of certificates and thus secure the vehicular communication in vehicular networks.