Abstract :

Cloud Computing Has Become An Essential Platform For Storing, Processing, And Managing Large Volumes Of Sensitive Data Across Various Domains Such As Healthcare, Finance, Education, And Enterprise Services. However, The Rapid Adoption Of Cloud Technologies Has Also Increased The Risk Of Cyber Threats, Particularly Privilege Escalation Attacks, Where Unauthorized Users Gain Elevated Access To Critical Cloud Resources. These Attacks Can Lead To Data Breaches, Unauthorized Modifications, And Severe Security Violations. To Address These Challenges, This Research Proposes A Machine Learning-based Framework For Detecting And Mitigating Privilege Escalation Attacks In Cloud Environments. The Proposed System Integrates Advanced Security Mechanisms Such As Encryption, Secure Authentication, And Intelligent Attack Detection Models To Identify Suspicious Activities In Real Time. Machine Learning Algorithms Are Trained On Cloud Access Patterns And User Behavior To Classify Legitimate And Malicious Actions With Improved Accuracy. The Framework Also Incorporates Secure Data Storage Techniques, Including Blockchain-inspired Integrity Mechanisms And Hash-based Verification, To Enhance Confidentiality And Prevent Unauthorized Modifications. The Developed System Provides Secure Communication Between Users And Cloud Servers Using Encryption Protocols And Multi-level Authentication Methods. Experimental Analysis Demonstrates That The Proposed Model Improves Attack Detection Efficiency, Minimizes False Positives, And Strengthens Overall Cloud Security. The System Is Scalable, Reliable, And Capable Of Protecting Sensitive Cloud Data Against Evolving Cyber Threats, Making It Suitable For Modern Cloud-based Applications And Enterprise Environments.

Published:

13-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

1106-1113

Section:

Articles

License:

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

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