Enhancing Cloud Security through Integrated Machine Learning Techniques: A Multi-layered Approach

Abstract

Cloud computing has become a fundamental platform for modern digital infrastructure, offering scalability, flexibility, and cost efficiency. However, the increasing adoption of cloud services has also introduced significant security challenges, including data breaches, unauthorized access, and sophisticated cyberattacks. Traditional security mechanisms often struggle to detect complex and evolving threats in dynamic cloud environments.

This study proposes a multilayered cloud security framework that integrates advanced machine learning techniques to enhance threat detection and system resilience. The proposed approach leverages multiple security layers, including anomaly detection, intrusion detection, and intelligent monitoring, to identify malicious activities in real time. By combining supervised and unsupervised machine learning models, the framework improves the accuracy of threat detection while reducing false positives.

The research highlights how integrating machine learning within cloud security architecture can provide adaptive and proactive protection against emerging cyber threats. The findings demonstrate that a multilayered intelligent security model can significantly strengthen the overall security posture of cloud infrastructures while ensuring efficient resource utilization and data protection.