Enhancing Cloud Security through Machine Learning-Based Threat Prevention and Monitoring: The Development and Evaluation of the PBPM Framework

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Binu C. T., S. Sarvana Kumar, Rubini P., Sudhakar K.


In the constantly evolving domain of cloud computing, ensuring the security of cloud infrastructures against complex cyber threats is crucial. This study presents the Push Back Threat Prevention and Monitoring (PBPM) framework, an innovative strategy to bolster cloud security. PBPM leverages cutting-edge machine learning algorithms alongside instantaneous threat detection and countermeasure mechanisms, constituting a formidable defence against potential security breaches. Through analyzing network traffic and user activity patterns, PBPM detects emerging security threats and proactively deploys defensive actions to neutralize risks. This forward-thinking approach to cloud security not only thwarts unauthorized access and data breaches but also maintains the resilience and accessibility of cloud services. Empirical evaluations, including simulations and deployment in practical scenarios, affirm the PBPM framework's efficacy in identifying and mitigating threats, marking a significant advancement over conventional security models. The outcomes of this research indicate that PBPM markedly diminishes the frequency of security incidents within cloud environments, providing a scalable and effective security solution for both cloud service providers and their clientele.

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