Learning Rich Features from Software-as-a-Service Cloud Computing for Detecting Trust Violations

We are witnessing a transition era of cloud security, as cloud computing paradigm is shifting its focus from provider to the consumer. Cloud service trust manipulation detection is different from traditional on-site service trust detection because cloud performs data operations at diverse geographically remote data centers; thus diminishing consumer control over the kind of service to be utilized. When the Cloud user submits a particular job to cloud, user has to rely upon the good behavior of the cloud to perform the task without violating the user trust in services utilized. However, due to lack of transparency in cloud, consumers find it hard to evaluate trust. Inspired by the recent progress of Spatial Rich Models (SRM) in image forensics domain, we propose to employ SRM and Machine learning approach to verify the trusted behavior of cloud by analyzing the rich features of the output produced by cloud service. We investigated noise distributions in data for violation detection. The approach is based on the hypothesis that every data processing task leaves certain distinct traces on the data. We identify those digital footprints to analyze whether cloud service provider has utilized the legitimate software-as-a-service for processing consumer requests. The inconsistency between authentic and obtained output acts as a proof-of-work for trust violation detection. The experimental results for the standard image dataset demonstrate that noise distributions in spatial domain can be successfully utilized to detect Cloud service trust violations.

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