Enhancing Cloud Security A Multi-Factor Authentication and Adaptive Cryptography Approach Using Machine Learning Techniques

The objective of this project is to create a robust security framework for cloud computing by integrating multi-factor authentication (MFA), adaptive cryptography, and machine learning. It aims to implement a multi-layer authentication system using passwords, fingerprint recognition, and conditional attributes for stronger access control. Additionally, the project focuses on enabling secure file uploads, where files are encrypted with AES and require either fingerprint authentication or OTP for decryption. The system will also introduce dynamic cryptography, adapting encryption algorithms based on predicted attack patterns. A Hybrid CNN-Transformer model will predict cyberattacks such as brute force and phishing, allowing proactive security adjustments. Continuous monitoring will ensure real-time adaptation of security protocols based on user behavior and threat predictions. The project will rigorously evaluate system performance, reliability, and resilience against threats to ensure robust data protection and prevent unauthorized access. Ultimately, the goal is to enhance cloud security and improve data integrity through an integrated approach.