Abstract
Intelligent transport systems are the future in matters of safe roads and comfortable driving. Integration of vehicles into a unified intelligent network leads to all kinds of security issues and cyber threats common to conventional networks. Rapid development of mobile ad hoc networks and machine learning methods allows us to ensure security of intelligent transport systems. In this paper, we design an authentication scheme that can be used to ensure message integrity and preserve conditional privacy for the vehicle user. The proposed authentication scheme is designed with lightweight cryptography methods, so that it only brings little computational and communication overhead. We also conduct experiments on vehicular ad hoc network segment traffic generation in OMNeT++ tool and apply up-to-date machine learning methods to detect malicious behavior in a given simulated environment. The results of the study show high accuracy in distributed denial-of-service attack detection.
Highlights
software-defined networks (SDNs) are mainly used in Vehicular distributed software-defined networks (VDSDNs) in stable parts of the network and in virtualization infrastructure, especially for Network Function Virtualization (NFV) modules and edge computing
SDNs are mainly used in VDSDNs in stable parts of the network and in virtualization infrastructure, especially for Network Function Virtualization (NFV) modules and edge computing
When an NFV module is used as an edge virtual machine or a container running on network equipment, the requirements for the selection and routing of network flows passing through this module can be implemented only by SDN infrastructures managed by the controller
Summary
In order to ensure secure communication between intelligently connected vehicles, a public key cryptography (PKC) mechanism was proposed. In [11], an anonymous authentication scheme based on certificateless PKC mechanism was proposed by using bilinear pairing operations. Generated scenario of multihop communication on ns-3 network simulator to detect wormhole attacks in VANET using KNN and support vector machine models. To study security issues in vehicular ad hoc networks and detect DDoS attacks, we make the following contributions: Authentication method: we proposed an anonymous authentication scheme based on elliptic curve encryption to meet the security requirements of vehicular ad hoc networks providing the least time cost on signing and verifying a message. Simulation of VANET dataset: using OMNeT++ simulation tool we simulated segment of VANET and implemented three types of popular network attacks which degrade overall performance of intelligent transport system by flooding vehicles with great amount of generated messages.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
