Published Paper Details:

As the different techniques such as Artificial Intelligence, Internet of Things, Blockchain revolutionized among today's generation, there is also an equally revolutionized technology which is Cloud Computing that is been used by millions of users for saving data, transferring data, and many more. As everything is getting advancing, there is also an important factor that every user's or say customers that are concerned with i.e., security. In data centers of massively scalable, the cloud services and data are residing and from everywhere it can be accessed. The increase of cloud customers has sadly been accompanied by an increase in malicious activity in the cloud. Almost every day more and greater vulnerabilities are discovered and published in a new security advisory. For different purposes Cloud surfing is done by millions of users; so that, they want highly persistent and safe services. So, in this paper we propose an effective enhanced security-based cloud computing using Cryptography and also Deep Learning (DL) approach was, Dense Belief Network is used as classifier layer for classifying various intrude data and then finally passed to Advance Encryption Standard before it is being launched to the cloud or ensuring data confidentiality and security. We also evaluated our proposed model (AES-RK-DBN) concerning other models such as LSTM, DenseNet, Resnet, VGG, CNN underperformance measures such as Accuracy, Specificity, Detection Rate, Sensitivity, and mostly Security in which our model gives higher satisfaction in terms of accuracy and security.