Machine learning with big data analytics for cloud security

Abstract

The amount of data generated and transmitted more quickly, particularly with the demand for action in real-time, has greatly increased with the growing number of internet-connected devices. With the rising diversity of data and need for data integrity, it is more challenging to accomplish this processing on time. However, cloud and edge computing pose security problems and time delays, as computing models are being applied rapidly. Hence in this paper, a Machine Learning-Assisted Cloud Computing Model (ML-CCM) with big data analytics has been proposed to increase security and improve data transmission rates. The simplest approach for storing large volumes of data is cloud storage. Big data can manage or store large amounts of distributed data in clouds. The ML algorithms analyze supervised and unsupervised training used to solve cloud protection problems. The experimental results show that ML-CCM has a data transmission rate of 96.4%, effective data management of 94.3%, computational time of 35.2%, accuracy of 91.7%, and performance of 95.2%.