DETECTING THE SECURITY LEVEL OF VARIOUS CRYPTOSYSTEMS USING MACHINE LEARNING MODELS

Abstract

In contemporary data management, cloud storage is widely adopted, yet it remains susceptible to security vulnerabilities. Utilizing cryptography techniques is essential for enhancing data security in such environments. One promising approach is hybrid cryptography, which combines multiple algorithms to bolster protection. Our proposed solution integrates RDH with DES algorithms, leveraging their respective strengths to fortify data security before it's stored in the cloud. Extensive testing has validated the efficacy of this approach. Specifically, we introduce an RDH with DES block-based transformation algorithm tailored for image content protection. Notably, our framework enables direct image retrieval and convolution on the content-protected images, enhancing usability alongside security. Keywords: deployment models, Infrastructure as a service, cryptosystems, machine learning