Data Classification on Data Confidentiality for Secure With Hybrid Chaotic Encryption in AWS Cloud Environment

Secure storage of sensitive data in hybrid cloud setups requires an advanced security system because of its growing adoption. This present paper implements a hybrid chaotic encryption and storage system within AWS's cloud architecture that includes data classification, chaotic key creation processes, and adaptive encryption systems. An automatic data classification system leads to High, Medium, and Low Confidentiality levels using machine learning approaches. Different encryption keys are generated from Logistic, Tent, and Hybrid chaotic maps according to the classification results. Data classification and encryption processes run automatically on AWS Lambda, and the encrypted information receives secure storage at Amazon S3. This study presents an analysis that reveals that the proposed algorithms perform better than Blowfish in terms of efficiency and entropy and ECC concerning energy efficiency and throughput. The designed encryption system offers real-time capabilities together with scalability and security, thus being suitable for financial applications, healthcare systems, IoT networks, and enterprise cloud environments.