Abstract :

Cloud Computing Has Become A Fundamental Technology For Delivering Scalable, Flexible, And Cost-effective Computing Services Across Various Industries. However, The Increasing Adoption Of Cloud Environments Has Also Introduced Significant Security Challenges, Including Unauthorized Access, Credential Theft, Data Breaches, And Sophisticated Cyberattacks. Traditional Authentication And Cryptographic Mechanisms Often Struggle To Adapt To Dynamic Threat Landscapes And Evolving Attack Strategies. This Paper Presents A Cloud Security Framework That Integrates Machine Learning-driven MultiFactor Authentication (MFA) With Adaptive Cryptography To Enhance The Protection Of Cloudbased Resources And Services. The Proposed Approach Utilizes Machine Learning Algorithms To Analyze User Behavior, Access Patterns, Device Characteristics, And Contextual Information To Strengthen Authentication Processes And Detect Anomalous Activities. Furthermore, Adaptive Cryptographic Techniques Dynamically Adjust Encryption Parameters Based On Risk Levels, Security Requirements, And System Conditions To Ensure Robust Data Protection. Experimental Analysis Demonstrates That The Integration Of Intelligent Authentication And Adaptive Encryption Significantly Improves Cloud Security, Reduces Unauthorized Access Risks, And Enhances Resilience Against Cyber Threats. The Proposed Framework Provides A Scalable, Intelligent, And Proactive Solution For Securing Modern Cloud Computing Environments.

Published:

26-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

391-403

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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