A Study of efficient and secure anonymous authentication scheme for IoT based pay TV systems

Abstract

With the increased integration of wireless communication technologies, the tendency of using mobile pay-TV (MPTV) services has increased noticeably in recent years. These integrated technologies provide convenience to the end-users to enjoy pay-TV services through mobile and home networks. Providing secure services to legitimate mobile users has become a major challenge in MPTV systems. Due to the open-medium nature of the interactions between the subscriber device and the head end system (HES), it is necessary to provide security protections in terms of authentication and privacy in an anonymous manner Unless a suitable anonymous authentication mechanism is provided, the MPTV system is vulnerable to various kinds of security attacks such as forging the user identity and illegal access of MPTV services. If authentication is not given anonymously, an illegal subscriber may impersonate as a legal subscriber to exploit or steal a service. Therefore, providing anonymous authentication becomes a necessary task for MPTV systems newlineOn the other hand, MPTV is a payment service necessitating the client to pay a price based on the subscriptions made. Customer Churn Prediction (CCP) is a hot research topic, which aims to identify the customers who are willing to terminate the subscription or moves to another service provider. Alternatively, churn prediction detects the customers who are possible to cancel a subscription to a service based on how they use the service. The CCP process can be considered as a data classification process and is used to allocate the data into two classes namely churner/non-churner. The advent of machine learning (ML) and deep learning (DL) models paves a way to resolve the data classification problem in MPTV systems. Besides, these models can provide an effective prediction process, and thereby the churners are identified with maximum churn detection rate. newline