Privacy-Preserving Threat Intelligence and Proactive Attack Mitigation in Distributed Cloud Infrastructure Using Advanced Machine Learning

Abstract

The rapid adoption of distributed cloud infrastructure has significantly increased cybersecurity challenges, particularly concerning sophisticated cyberattacks, unauthorized access, data breaches, and privacy violations. Traditional security mechanisms often struggle to identify emerging threats across geographically distributed cloud environments because of fragmented monitoring systems, limited threat intelligence sharing, and increasing volumes of complex security data. This study explores a privacy-preserving threat intelligence and proactive attack mitigation framework using advanced machine learning techniques to improve cybersecurity in distributed cloud infrastructure. The proposed framework integrates federated learning, anomaly detection, behavioral analytics, encryption-based data protection, and intelligent threat classification to identify malicious activities without requiring the centralization of sensitive organizational information. Furthermore, machine learning models analyze network traffic patterns, access behaviors, and system activities to detect potential attacks and support automated security responses. The research methodology proposes an experimental evaluation using representative cloud security datasets and simulated distributed environments. Performance is assessed through detection accuracy, precision, recall, F1-score, false-positive rate, detection latency, and communication overhead. The proposed approach aims to demonstrate how collaborative intelligence and privacy-preserving computation can strengthen threat detection while maintaining data confidentiality. The study contributes an integrated conceptual framework for improving security visibility, reducing attack response time, and supporting resilient, scalable, and privacy-aware cloud infrastructure