Securing Software-Defined Vehicular Network Architecture against DDoS attack

Abstract

In the recent decades, Intelligent Transport Systems (ITS) attracted researchers’ great attention. ITS plays a very important role in making citizens’ lives easier in term of mobility, safety, quality of life and security. Vehicular ad-hoc networks (VANETs) became an inseparable component of ITS. The current architecture has been facing many issues due to VANET’s characteristics such as high mobility of its nodes and it is still vulnerable to important security attacks which threatens its main security services such as availability, data integrity, authentication and privacy. We propose a new VANET architecture called FCSDVN-ML, in which we combine three emerging paradigms: Software-Defined Network (SDN), Fog Computing (FC) and Machine Learning (ML) to improve security in VANETs. In this paper, we described our architecture components and we discussed its potential performance against Distributed Denial of Service (DDoS) attack using the hierarchical firewalls.