Intelligent Adaptive Cloud Security Architecture Using Federated Machine Learning for Privacy-Preserving Threat Intelligence

Abstract

The increasing adoption of cloud computing, distributed applications, multi-cloud infrastructures, and interconnected enterprise platforms has created complex cybersecurity challenges that require intelligent and privacy-preserving threat detection mechanisms. Conventional centralized machine learning approaches require organizations to transfer sensitive security data to common repositories, potentially increasing privacy risks, data exposure, and regulatory challenges. This research proposes an Intelligent Adaptive Cloud Security Architecture Using Federated Machine Learning for Privacy-Preserving Threat Intelligence. The proposed architecture enables multiple cloud environments and participating organizations to collaboratively train machine learning models without directly sharing their raw security data. Local models analyze network traffic, authentication events, system logs, API activities, endpoint behavior, and other security indicators, while only protected model updates are exchanged through a federated coordination layer. Secure aggregation, encryption, differential privacy, identity management, and Zero Trust principles are incorporated to protect the collaborative learning process. The architecture also introduces adaptive threat intelligence capabilities that continuously identify emerging attack patterns and update security policies according to changing risk conditions. Experimental evaluation considers detection accuracy, precision, recall, F1-score, communication overhead, convergence time, privacy protection, latency, scalability, and resilience against malicious participants. The proposed framework aims to provide collaborative, adaptive, and privacy-aware cybersecurity intelligence while reducing the risks associated with centralized security-data sharing across distributed cloud environments.