More connected devices bring amazing benefits to people and enterprises. However, they also create more digital doorways. The risk in IoT is not just financial. IoT connecting medical devices, running city infrastructure and even the houses we sleep in. Connected gadgets and sensors in our homes and working environments known as the Internet of Things-offer gigantic potential for improving how internet live and move around. We can quantify wellbeing information, travel propensities and vitality use. In any case, as more gadgets become associated, vulnerable they are to complex digital security dangers. Connected devices and sensors in our homes and workplacesknown as the Internet of Things-offer huge potential for improving how we live and move around. We can measure health data, travel habits and energy use. But as more devices become connected, the more vulnerable they are to sophisticated cyber security threats. There exist a few application security issues; for example, data access and user authentication, data protection, decimate and track of information stream, IoT platform stability, middleware security, the executives stage, etc. An effective trust management model is to be used in each IoT framework to ensure the framework against malevolent assaults and consequently ensuring dependable and secure data transmission. To achieve this objective, various trust management models are used to enforce different security measures in a social IoT system. Two different trust management models namely dynamic model and machine learning based model are clarified and correlation of model are expressed and along these lines the benefit of one model over the other is comprehended.. Appropriately in this paper, a detailed study of each model is done with other pinpoints thus leading to a thorough study of two diverse trust management models.
GROUP KEY MANAGEMENT FOR MULTICAST STRUCTURES TO IMPROVE SECURED DATA ACCESS
