Trust management in the internet of everything: A review of deep-learning based solutions

Internet of Everything (IoE) is an extension of Internet of Things (IoT) that has revolutionized the digitalization of our society by connecting things and humans to create a digital world. As a result, IoE has reshaped the way that humans and autonomous systems interact and exchange information in network connections. However, the digitization imposes new security, trust, and safety concerns, such as the risk of undesirable behavior of driverless cars, which might cause dangerous road consequences. In this context, Deep-Learning (DL) applications have emerged in trust management to proactively improve the efficiency, reliability, security, trust, and safety of different IoE entities, and then achieve the sustainability of different social IoE relationships. To support further progress in integrating DL-based solutions in trust management, this paper reviews the existing literature related to DL-based trust management. Specifically, the review targets the DL-based approaches employed in trust management, their components and application areas in the context of IoE, and trust relationships in IoE. Furthermore, it highlights a set of research challenges, opportunities and recommendations related to the adoption of proactive trust management using deep learning in IoE.