Improving Existing Data Security Standards in Cloud Computing Using Trust Based Machine Learning

Improving Existing Data Security Standards in Cloud Computing Using Trust Based Machine Learning

Journal

Subject and more

  • LCC Subject Category: Engineering, Applied Linguistics
  • Publisher's keywords: Cloud Computing, Big Data, Artificial Intelligence, Random Forest Tree, Machine Learning, AES
  • Language of fulltext: english
  • Full-text formats available: PDF
  • Time From Submission to Publication: 8

AUTHORS

    Aditya Chellam

FULL TEXT

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ABSTRACT

The onset of the Big Data phenomenon, has brought to light the need to store and process data externally for effective and efficient computation. Cloud computing is a technology that has enabled individual users and organizations alike to implement such a functionality. Currently, a large percentage of the data being generated is stored on clouds and the number of organizations opting for cloud based technologies is constantly on the rise. With such growing numbers accessing and utilizing cloud resources, data security has become a major cause of concern. Traditional methods of cloud computing are becoming obsolete and ineffective with each technological breakthrough and data is thus highly subjected to getting corrupted or hacked. Machine Learning algorithms can be implemented to program the security mechanism such that the cloud is able to verify and secure the data with greater efficiency and improve the security predictions as more and more data gets accumulated. An AI (Artificial Intelligence) driven framework for cloud computing, can not only handle the current data traffic but is also a viable framework for the future as it learns and improves itself constantly as the accreted knowledge base increases. This paper implements Random Forest machine learning algorithm to improve the data security in cloud computing.

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