Machine Learning and Deep Learning for Cloud Computing Security
Dr.S.Thilagavathi Dr.S.Thilagavathi, Rithick R Rahul Rithick R Rahul · International Journal of Advances in Engineering and Management · 2025
The escalating complexity of cyber threats in cloud computing environments necessitates innovative approaches for robust security measures. This paper explores the integration of machine learning algorithms as a proactive strategy to fortify cloud computing security. The abstract delves into the diverse applications of machine learning, including anomaly detection, threat identification, and behavioural analysis, within the context of cloud security. The paper evaluates the efficacy of supervised and unsupervised learning models, highlighting their adaptability to dynamic threat landscapes. Additionally, the abstract discusses the role of machine learning in enhancing real-time incident response and the potential for continual learning to stay abreast of evolving security challenges. By examining the symbiotic relationship between machine learning and cloud security, this paper aims to provide a comprehensive overview of state-of-the-art methodologies, offering insights into the evolving landscape of secure cloud computing