Abstract: The increasing intricacy of cyber threats within cloud computing environments demands novel strategies for strong security protocols. To strengthen cloud computing security, this paper investigates the proactive approach of integrating machine learning algorithms. Within the framework of cloud security, the abstract explores the various uses of machine learning, such as anomaly detection, threat identification, and behavioral analysis. The study assesses the effectiveness of both supervised and unsupervised learning models, emphasizing how flexible they are in response to changing threat environments. The abstract also covers the potential for continuous learning to keep up with changing security challenges and how machine learning can improve real-time incident response. By looking at how cloud security and machine learning work together. This paper attempts to give a thorough overview of contemporary approaches and insights into how secure cloud computing is developing.

Keywords: Supervised Learning, Unsupervised Learning, Real-time Incident Response, Continual Learning, Cybersecurity.

| DOI: 10.17148/IMRJR.2024.010106