Operator and Manufacturer Independent D2D Private Link for Future 5G Networks

Direct Mobile-to-Mobile communication mode known also as Device-to-Device (D2D) communication is expected to be supported in the 5G mobile system. D2D communication aims to improve system spectrum efficiency, overall system throughput, energy efficiency and reduce the connection delay between devices. However, new security threats and challenges need to be considered regarding device and user authentication to avoid unauthorized access, abuse and attacks on the whole system. In this paper, a strong standalone authentication technique therefore is proposed. It is based on combining users biometric identities and a new clone-resistant device identity. The novel property of the proposal is that it is fully independent on both device manufacturer and mobile system operator. The biometric identity deploys user keystroke dynamics and accelerometer to generate user biometric identity by deploying a machine learning technique. The proposed mobile device clone-resistant identity is based on deploying a new concept of a pure digital clone-resistant structure which is both manufacturer and mobile operator-independent. When combining both identities, a mutually authenticated D2D secured link between any two devices can be established in addition to a strong user-device authentication. Furthermore, the concept does not allow the managing trusted authority to intercept users private links. Being an independent and standalone system, the technique would offer a broad spectrum of attractive future smart applications over the 5G mobile system infrastructure.Comment: 6 pages, 7 page

Machine Learning (ML) In a 5G Standalone (SA) Self Organizing Network (SON)

Machine learning (ML) is included in Self-organizing Networks (SONs) that are key drivers for enhancing the Operations, Administration, and Maintenance (OAM) activities. It is included in the 5G Standalone (SA) system is one of the 5G communication tracks that transforms 4G networking to next-generation technology that is based on mobile applications. The research's main aim is to an overview of machine learning (ML) in 5G standalone core networks. 5G Standalone is considered a key enabler by the service providers as it improves the efficacy of the throughput that edges the network. It also assists in advancing new cellular use cases like ultra-reliable low latency communications (URLLC) that supports combinations of frequencies.Comment: 5G, Machine learning (ML), Self-organizing Networks (SONs), 5G Standalone, Artificial Intelligence (AI