Securing Multicloud Environments With SAFIRE: A Federated and Adaptive Intelligence Approach

ABSTRACT Multicloud environments provide multicloud environment provision of cyber‐attacks demands to have flexible security mechanisms, which can dynamically respond to evolving patterns of attacks. In this paper, a novel framework of Self‐Adaptive Federated Intelligence known as Self‐Adaptive Federated Intelligence of Real‐time security Enforcement (SAFIRE) is introduced, which implements a combination of real‐time security intelligence extraction, cross‐cloud threat correlation, and adaptive learning to provide a more efficient security solution. This model uses a security insight system that trains itself to analyze multicloud attack patterns dynamically in order to provide real‐time detection of advanced threats. A dynamic learning mechanism that provides changes in the dynamic trends in security decision‐making is an important aspect of the model. A hierarchical classification module also divides the different types of attacks and corrects mitigation measures based on this. By employing an attention‐based system of cross‐cloud adaptation, the suggested system will enable a number of cloud service providers to collaborate toward greater levels of security in a noncentralized fashion. The key strength of this work is its potential to trace the pattern of multicloud attacks, adjust security policy in real‐time situations, and enhance its threat detection with limited reliance on the centralized view of data accumulation. As experimental findings show, the proposed methodology is more accurate (98.9%), less prone to false positives (1.9), lower response time (180 ms), and less resource‐intensive (4%). The findings indicate that the model takes minimal time to adapt to emerging cyber‐attacks with high detection and low overhead rates and is therefore interesting as a solution to secure cloud infrastructure of the future.