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.