Enhanced intelligent data security model for monitoring cloud computing infrastructure using machine learning algorithm

Abstract

Cloud computing has transformed data management with its scalability and cost-effectiveness, but it also introduces significant security risks, including data breaches, unauthorized access, and malicious attacks. Traditional security approaches often fail to detect sophisticated attacks, highlighting the need for intelligent systems to learn and adapt to evolving threats. This study presents a novel data security virtualization model that employs the Random Forest algorithm to enhance the security of cloud computing infrastructures. The model combines network traffic analysis, system log analysis, and virtual machine monitoring to detect and respond to security threats. The study aims to improve the accuracy and efficiency of threat detection and response using machine learning techniques. The proposed model was evaluated using a comprehensive dataset and showed promising results, achieving high accuracy in detecting malicious activity with a precision of 0.99 and recall of 0.99. This research contributes to proactive security measures in cloud computing by integrating advanced machine learning methodologies, providing a novel solution that addresses the limitations of traditional security approaches.