Visible to the public A Security System Using Deep Learning Approach for Internet of Vehicles (IoV)

TitleA Security System Using Deep Learning Approach for Internet of Vehicles (IoV)
Publication TypeConference Paper
Year of Publication2018
AuthorsSharma, Sachin, Ghanshala, Kamal Kumar, Mohan, Seshadri
Conference Name2018 9th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON)
Date PublishedNov. 2018
ISBN Number978-1-5386-7693-6
Keywordsconnected vehicles, connected vehicles communication, critical V2X infrastructures deployment, Deep Learning, deep learning approach, deep learning methodology, human factors, Internet of Things, Internet of Vehicles, IoT, IoV security system, learning (artificial intelligence), Metrics, mobile computing, neural nets, pubcrawl, reliable communication services, reliable services, resilience, Resiliency, secure applications, security of data, security threats, smart cities, smart environments, smart offices, Smart Spectrum Utilization, spectrum utilization, supervised learning, unsupervised learning

The Internet of Vehicles (IoV) will connect not only mobile devices with vehicles, but it will also connect vehicles with each other, and with smart offices, buildings, homes, theaters, shopping malls, and cities. The IoV facilitates optimal and reliable communication services to connected vehicles in smart cities. The backbone of connected vehicles communication is the critical V2X infrastructures deployment. The spectrum utilization depends on the demand by the end users and the development of infrastructure that includes efficient automation techniques together with the Internet of Things (IoT). The infrastructure enables us to build smart environments for spectrum utilization, which we refer to as Smart Spectrum Utilization (SSU). This paper presents an integrated system consisting of SSU with IoV. However, the tasks of securing IoV and protecting it from cyber attacks present considerable challenges. This paper introduces an IoV security system using deep learning approach to develop secure applications and reliable services. Deep learning composed of unsupervised learning and supervised learning, could optimize the IoV security system. The deep learning methodology is applied to monitor security threats. Results from simulations show that the monitoring accuracy of the proposed security system is superior to that of the traditional system.

Citation Keysharma_security_2018