A Way to Protect the Surveillance Data Stored in the Cloud Using FC and SVM Methods

Building trust between consumers and service providers is critical to the functioning of cloud computing, a technology that offers a range of services over the Internet. When consumers choose which cloud services to utilise for processing and storing sensitive data, trust values are a major factor. In addition, service providers can employ trust values as a means of granting access to resources, protecting against malevolent users. The subjective trust model category, which evaluates trust based on user and service provider behaviour, is the one that this research suggests for trust models. Because trust is intrinsically ambiguous in our method, trust levels for cloud consumers and service providers are calculated using fuzzy logic and support vector machines. Using triangle membership functions for defuzzification and Gauss membership functions for fuzzification, we use the Mamdani fuzzy approach. In order to determine how trustworthy a resource is, our paradigm for evaluating trust takes performance and flexibility into account. Performance evaluation includes measurements related to workload and reaction time, whereas elasticity evaluation takes into account elements like scalability, availability, security, and usability. We apply fuzzy C-means clustering on pertinent criteria to establish trust levels for users, including faulty requests, fake requests, unauthorised requests, and total requests.