A NOVEL INTEGRATION OF MACHINE LEARNING -BASED DATA CLASSIFICATION WITH OPTIMIZED CRYPTOGRAPHIC TECHNIQUES FOR SECURE CLOUD STORAGE

Security measures for cloud storage remain crucial because modern data management depends heavily on such storage, yet it exposes vulnerable data points. The proposed hybrid security framework combines machine learning methodologies with cryptographic protocols to improve cloud environment data protection. The proposed system utilizes Support Vector Machine (SVM) and Random Forest (RF) machine learning classifiers that sort data into three security levels: public, private, and confidential through established security attribute definitions. We employ specific encryption techniques, which include RSA and AES for moderately sensitive data, whereas ChaCha20-Poly1305 encrypts high-security sensitive information. Data integrity protection through the ChaCha20-Poly1305 scheme exists because it combines encryption with authentication that adds a message authentication code (MAC) derived from the ciphertext and keystream. The selective encryption approach achieves better computational speed because it avoids unnecessary waste of processing resources through complex encryption protocols. Testing cloud-stored databases showed our combination method delivers faster performance during encryption time of 5-10% improvement than independent ChaCha20 implementation. This framework delivers improved scalability as well as security-efficiency balances that make it appropriate for actual cloud storage applications.