Machine Learning Based Efficient and Secure Storage Mechanism in Cloud Computing
Kanav Sadawarti1, Satish Saini2

1Kanav Sadawarti*, Scholar, Computer Science & Engineering, RMIT, Mandi, Gobindgarh, India.
2Dr. Satish Saini, Electronics communication & Engineering, RMIT, Mandi, Gobindgarh, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 10, 2020. | PP: 195-199| Volume-9 Issue-5, March 2020. | Retrieval Number: D1359029420/2020©BEIESP | DOI: 10.35940/ijitee.D1359.039520
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The cloud is an online platform that offers services for end-users by ensuring the Quality of services (QoS) of the data. Since, the user’s access data through the internet, therefore problem like Security and confidentiality of cloud data appears. To resolve this problem, encryption mechanism named as Rivest–Shamir–Adleman (RSA) with Triple Data Encryption Standard (DES) approach is used in hybridization. This paper mainly focused on two issues, such as Security and Storage of data. The Security of cloud data is resolved using the encryption approach, whereas, the data storage is performed using Modified Best Fit Decreasing (MBFD) with Whale Optimization algorithm (WOA)&Artificial Neural Network (ANN) approach. The neural network with the whale as an optimization approach model makes sure the high confidentiality of cloud data storage in a managed way. From the experiment, it is analyzed that the proposed cloud system performs better in terms of energy consumption, delay, and Service Level Agreement (SLA) violation. 
Keywords: Cloud storage, Security, encryption, Rivest–Shamir Adleman, Triple Data Encryption Standard, Modified Best Fit Decreasing, Whale Optimization Algorithm, Artificial Neural Network
Scope of the Article: Artificial Intelligence and Machine Learning