Strengthening Data Protection in Cloud Platforms through Multi-Step Verification and Intelligent Encryption Techniques

The fast growth of cloud computing shows how important it is to have strong security measures in place to protect sensitive data on remote servers. Authentication is very important for keeping this information safe. Even though different methods have been suggested, weaknesses still exist. This article presents an innovative multi-factor authentication system that incorporates a hybrid cryptographic framework, which adaptively alters encryption algorithms through machine learning techniques informed by an intrusion detection system. The suggested system uses passwords, conditional attributes, and fingerprint authentication to get the encryption key from fingerprint data. It uses a dual-encryption method that combines five pairs of algorithms: AES + HMAC (SHA-256), ECC + HMAC (SHA-512), HMAC-MD5 + PBKDF2, Twofish + Argon2, and Blowfish + HMAC SHA3-256. A Hybrid CNN-transformer model predicts and sorts attacks by changing an encryption algorithm on the fly to protect the data. The framework was very strong against attacks like brute force, spoofing, phishing, guessing, and impersonation. The proposed model had an impressive accuracy rate of 96.8%, which was better than that of other models. Using this framework in a cloud authentication setting greatly improves data privacy and stops people from getting into your account without permission. This study shows that combining multi-factor authentication and adaptive cryptography could lead to strong cloud security solutions.