Abstract :

The Rapid Growth Of Cloud Computing Has Introduced Significant Security Challenges, Particularly In The Detection And Prevention Of Cyberattacks. Traditional Security Mechanisms Often Fail To Identify Sophisticated And Evolving Threats Such As Distributed Denial Of Service (DDoS), Phishing, And Intrusion Attacks In Real Time. To Address These Issues, This Paper Proposes An Advanced Machine Learningbased Approach For Cyberattack Detection In Cloud Computing Environments.The Proposed System Leverages Multiple Machine Learning Algorithms, Including Supervised And Unsupervised Techniques, To Analyze Large Volumes Of Network Traffic And Identify Abnormal Patterns Indicative Of Malicious Activities. Feature Selection And Data Preprocessing Techniques Are Applied To Improve Detection Accuracy And Reduce False Positives. The Model Is Trained On Benchmark Datasets And Evaluated Using Performance Metrics Such As Accuracy, Precision, Recall, And F1-score. Additionally, The System Incorporates Real-time Monitoring And Adaptive Learning Capabilities To Detect Both Known And Unknown Attacks Effectively. By Continuously Updating The Model With New Data, The System Enhances Its Ability To Respond To Emerging Threats. The Integration Of Machine Learning With Cloud Infrastructure Provides A Scalable, Efficient, And Automated Solution For Improving Cybersecurity. Experimental Results Demonstrate That The Proposed Approach Significantly Outperforms Traditional Detection Methods In Terms Of Accuracy, Detection Rate, And Response Time. This Makes It A Reliable Solution For Securing Cloud Environments Against Modern Cyber Threats.

Published:

15-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

2280 - 2288

Section:

Articles

License:

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

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