A new Intrusion Detection Framework for Vehicular Networks

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.

I. Introduction

Intelligent Transportation Systems (ITS) have been increasing rapidly and Vehicle Ad hoc Networks (VANETs) are considered as a main system for the deployment of ITS-based applications. Furthermore, many services have been developed for vehicular networks, which are sorted in two classes: infotainment and safety services. Most of them rely on various types of data collected and/or disseminated from/to vehicles (ex. Cooperative Awareness Messages - CAM). We can cite as examples the services developed by the CarCoDe project [1]: real-time traffic monitoring and management, local service delivery, and real-time fleet management. Securing these networks is then becoming an important challenge since they are more attractive to attacks due to the relevant information that vehicles can manage. Therefore, our aim is to propose an efficient security mechanism to protect such networks against intruders, taking as a use case the real-time traffic monitoring and management service.