Distributed Cloud Threat Detection Through Privacy-Aware Machine Learning and Cross-Enterprise Security Analytics

Abstract

The rapid expansion of distributed cloud computing has increased the complexity of cybersecurity management across interconnected enterprise environments. Organizations increasingly depend on public clouds, private clouds, hybrid infrastructures, and shared digital services to process sensitive information and support critical business operations. However, fragmented security monitoring, heterogeneous data formats, limited threat intelligence sharing, and privacy restrictions can prevent organizations from identifying coordinated cyberattacks effectively. This research proposes a privacy-aware machine learning framework for distributed cloud threat detection and cross-enterprise security analytics. The framework integrates distributed telemetry collection, privacy-preserving data processing, collaborative model training, behavioral anomaly detection, and intelligent threat correlation. Federated learning enables participating organizations to collaboratively improve threat detection models without directly exchanging their raw security logs. Privacy-enhancing mechanisms, including data minimization, pseudonymization, secure aggregation, and differential privacy where appropriate, are incorporated to reduce exposure of sensitive information. Machine learning models analyze authentication events, network traffic characteristics, workload behavior, and access patterns to identify suspicious activities across distributed environments. A cross-enterprise analytics layer correlates model outputs and contextual indicators to recognize potential attack campaigns that may remain undetected within individual organizations. The proposed methodology evaluates detection precision, recall, false-positive rates, communication overhead, computational cost, and privacy protection. Comparative experiments against centralized and independent local detection approaches are designed to assess effectiveness and operational feasibility. The framework aims to improve collaborative cyber defense while preserving organizational data sovereignty, supporting scalable threat intelligence sharing, and strengthening resilience against evolving threats across distributed cloud enterprise systems.