Performance Enhancement Techniques In Hybrid Cloud Computing

Abstract

The security concerns that are associated with cloud computing are investigated in this paper. The usage of data encryption and firewall security technologies has been used in a number of research projects in order to enhance protection. There is a possibility that performance may suffer if security is emphasized more, which is one of the issues that has been seen. As a consequence of this, a solution is needed that has the potential to either hold or improve performance while simultaneously providing better security features. In addition, machine learning has been used to differentiate between methods of data transportation that are safe and those that are not secure. When it comes to the hybrid approach, the combination of encryption and machine learning ultimately produces the desired outcomes, which are enhanced security without compromising efficiency. Combining a number of different strategies, such as AES encryption, replacement-based compression, and an LSTM machine learning model, is what the hybrid technique does.