Secure information processing for multimedia forensics using zero-trust security model for large scale data analytics in SaaS cloud computing environment

Trust is an essential security component and plays a crucial role in building secure information processing systems. Many organizations have adopted zero-trust principles in their security mechanisms, especially in Cloud Computing environments, where everything is presumed to be semi-honest and untrustworthy until proven otherwise. This is in blunt contrast to the traditional perimeter-security model, where it is presumed that the malicious actors are always outside the perimeter of the trusted network, and trustworthy users are always inside the network. One of the most significant challenges with the notion of trust is that till now more focus was put on the subjective aspect of trust which was challenging to evaluate and verify. There is an urgent need to address the objective and verifiable notion of trust, especially in the systems involving Federated Learning as in Cloud environments. We propose a Rich model based zero-trust security framework for trust verification of SaaS and employ machine learning functionalities to perform multimedia data analytics over the service processing behaviors for improved visibility into service operations and risks. Our proposed model relies heavily on the features extracted form Federated learning on the data of different cloud service users that is subjected to AI processing. We utilized the Rich models for feature extraction of large scale multimedia data for monitoring cloud service behavior and employed Ensemble classifier for analyzing the data features and employed majority voting of individual learners for decision making. The experiments performed on the standard dataset proved that the proposed model successfully monitored and analyzed the behavior of cloud services for validating the legitimacy of trusted SaaS and detecting any trust violations.