Published Paper Details:

In recent years, multiple well-established business sectors, especially those where internet enterprises are popular, especially Google, Amazon, etc., have primarily turned to cloud technology for data retention and other reasons. Customers of cloud systems can take advantage of their simple processes, affordable setup, and cheap maintenance costs. The information protection practises of cloud services do, however, present serious threats. The issue of the security of cloud data and customer dependability remains dubious despite the constant analysis and reformation of the sector because of the proliferation of cyberattack techniques and problems with cloud storage platforms. This research provides a machine learning-based cloud data protection and search model that is impacted by balanced searchable encoding in order to address this danger and add to the effort of offering perfect information safety methods in methods of storing and retrieving data in the cloud. The proposed approach applies an accurate term ranking technique while enhancing private information through the use of an ANN. The proposed approach has superior functional ability when compared to multilevel SVM and Naive Bayes, according to a comparative study. The correlation between low FPR and high TPR supports the efficacy of the planned effort. A low CCR of 0.6972 further contributes to the efficacy of the suggested work.