"ADAPTIVE AND DYNAMIC SECURITY IN CLOUD COMPUTING THROUGH MACHINE LEARNING INTEGRATION"

Abstract

This research delves into the integration of machine learning (ML) to foster adaptive and dynamic security in cloud computing environments. As the adoption of cloud services surges, the complexity and diversity of security threats have grown, rendering traditional static security measures inadequate. This study investigates the application of various ML algorithms to enhance security mechanisms, enabling real-time threat detection and response. By analyzing historical security data and employing adaptive learning techniques, the models evolve continuously to address emerging threats. The findings reveal significant improvements in threat detection accuracy and response times, underscoring the critical role of ML in developing resilient cloud security frameworks. By leveraging ML techniques, this research aims to bolster the detection, prediction, and mitigation of security threats, providing a proactive and robust approach to cloud security. The study offers a comprehensive framework for implementing ML-driven security strategies, highlighting their potential to transform cloud security practices.