Trust-Based Chaos Access Control Framework by Neural Network for Cloud Computing Environment

Abstract

Trust between users and Cloud Service Providers (CSP) is an important attribute in the cloud computing environment. Cloud trust is directly proportional to data security and data privacy in such a way that it affects the quality of service (QoS). Lack of control over data content and transparency in processing of data by the CSP could diminish the trust factor. In this chapter, Trust-Based Chaos Access Control (TBCAC) framework for cloud environment is proposed and evaluated through MATLAB. It is a neural network (NN) based framework that monitors, analyses, and enforces the trust requirements for data publication. The trust manager proposed in this chapter works well in a real environment and it acquires trust-related data regarding every cloud user’s transaction on the cloud server. The cloud manager used in the proposed architecture is competent for dynamically analyzing the data requesting pattern from the user through NN-based classifiers. The proposed architecture is evaluated using MATLAB environment with Amazon cloud services dataset for the demonstration purpose. The results prove that TBCAC effectively prevents the unauthorized behavior and access in the cloud computing environment.