A new intrusion detection framework for vehicular networks

Abstract

In this paper, we design and implement a new Intrusion Detection Framework for Vehicular Networks (IDFV). These networks are vulnerable to various security attacks due to the lack of centralized infrastructure. The aim of our framework is then to secure them against the most dangerous routing attacks that have a high severity damage such as selective forwarding, black hole, wormhole, packets duplication and resource exhaustion attacks that can target such networks. IDFV relies on a set of detection and eviction techniques to detect, in a short delay, malicious vehicles with a high accuracy and eject them. Furthermore, IDFV applies a robust reputation schema to evaluate vehicles' trust level. We analyze the performances of our framework using NS-3. Simulation results show that IDFV exhibits a high level of security i.e. high detection rate, low false positive rate and a fast attacks' detection compared to detection frameworks proposed in current literature. © 2014 IEEE.