Advanced Threat Detection Mechanisms for Cloud Security: A Machine Learning Perspective

Abstract

The rapid adoption of cloud computing has led to increased exposure to sophisticated cyber threats, necessitating advanced security mechanisms. This paper explores cutting-edge machine learning techniques for threat detection in cloud environments, focusing on their ability to identify, predict, and mitigate potential cyber-attacks. Machine learning models such as deep learning, support vector machines, and ensemble methods are evaluated for their effectiveness in detecting malware, unauthorized access, and other security breaches. By leveraging large datasets and real-time data streams, these models can continuously improve their detection capabilities, providing a proactive approach to threat identification. Key performance metrics such as accuracy, false-positive rates, and response time are analyzed to determine the best-fit algorithms for cloud security applications. The integration of machine learning with traditional cloud security measures is examined to create a multi-layered defense system. The paper also discusses the challenges of implementing machine learning in cloud environments, including scalability, computational cost, and data privacy concerns. Through a comprehensive analysis, this study highlights the potential of machine learning as a transformative tool for enhancing cloud security, offering both theoretical insights and practical solutions for safeguarding cloud infrastructure against evolving cyber threats.