Secure Cloud Framework Based on Machine learning Approach

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Prasenjit kumar Das, Nidul Sinha, Annappa B


In the present business scenario, Cloud Computing has taken a centerstageduetoitscost-effectiveness,efficiency,andscalability.Therehasbeenbroaduseof cloud-based systems and its services by most of the organizations in current times.And in order to safeguard the different transactions of information over the cloudenvironment,itisverymuchessential toprovideasecureplatformfortheusers.Therefore cloud security plays a significant role in ensuring confidentiality, integrity,andavailabilityofinformation.Thispapermainlyfocusedontheuseof Machinelearning(ML)algorithms as atool tosecuredatastoredin thecloud.It is worthmentioning that Machine learning has been widely used in analyzing data anomalies,predicting threats, classify data’s, etc. in cloud-based system. The main objective of thispaperistoproposeasecurecloudframeworkwithtwodistinguishedparti.e.classification and encryption. Here we mainly focused on the classification of data usingone of the Machine Learning Algorithm i.e. Hybrid Naïve Bayes Algorithm where dataareclassified into threelevels viz. basic, sensitive, and highly sensitive. The proposedML algorithm is experimented using cloudsim simulating tool, results are analyzed andcompared with existing other ML classification algorithms namely K-Nearest Neighbor(KNN)andSupportVectorMachine(SVM).

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