Trust-Based Access Control in Cloud Computing Using Machine Learning

  • Pabitr Mohan KhilarEmail author
  • Vijay Chaudhari
  • Rakesh Ranjan Swain
Part of the Studies in Big Data book series (SBD, volume 49)


Cloud computing is a distributed computing environment which hosts dedicated computing resources accessed anytime from anywhere. This brings many advantages such as flexibility of data access, data omnipresence, and elasticity [1, 2, 3, 4, 5, 6, 7]. As there is no control of data owner over the data, this brings security threats. Providing a secure cloud environment from the malicious user is one of the important and challenging tasks among scientific and business user community. Over the time, various control access models have been proposed for secure access in the cloud environment such as cryptographic-based access model, identity-based access control model and trust-based access control model. The users and cloud resources should be trusted before accessing the cloud. It is observed that the existing access control models mainly overlook the user behavior and scalability of the trust management system. We have considered the trust-based approach which provides access to the user in the cloud by their trust value computed based on the past accesses and behavior. We consider important parameters such as user behavior, bogus request, unauthorized request, forbidden request and specification of range. We proposed a trust evaluation strategy based on the machine learning approach predicting the trust values of user and resources. The machine learning techniques such as K-Nearest neighbor, decision tree, logistic regression and naive Bays are considered as the important strategies to evaluate the trust management system in our proposed work. We implemented our proposed machine learning method in jupyter notebook simulator tool. We found better result in terms of efficiency, prediction time and error rate which is presented in the result section of this chapter.


Cloud computing Machine learning Access control Cryptography based access model 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pabitr Mohan Khilar
    • 1
    Email author
  • Vijay Chaudhari
    • 1
  • Rakesh Ranjan Swain
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of Technology RourkelaRourkelaIndia

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