Adaptive Enterprise Cloud Security Through Federated Threat Intelligence and Machine Learning-Based Risk Prediction
DOI:
https://doi.org/10.15662/IJEETR.2026.0805009Keywords:
Adaptive Cloud Security, Federated Learning, Threat Intelligence, Machine Learning, Enterprise Risk Prediction, Collaborative Cybersecurity, Privacy Preservation, Distributed Threat Detection, Cloud Risk Assessment, Intrusion DetectionAbstract
The rapid expansion of enterprise cloud computing has increased cybersecurity complexity by introducing distributed infrastructure, dynamic workloads, diverse access environments, and continuously evolving attack surfaces. Conventional centralized security approaches often face limitations related to data privacy, delayed threat intelligence sharing, scalability, and the timely identification of emerging risks. This study proposes an adaptive enterprise cloud security framework that integrates federated threat intelligence with machine learning-based risk prediction to strengthen collaborative threat detection and proactive risk management. The proposed framework enables participating organizations or cloud environments to collaboratively improve threat detection models without directly exchanging raw security data. Federated learning supports decentralized model training, while threat intelligence sharing provides contextual information about malicious indicators, attack patterns, vulnerabilities, and emerging adversarial techniques. Machine learning algorithms are employed to classify suspicious activities, estimate threat probabilities, and prioritize cloud assets according to their security exposure and business criticality. The methodology incorporates data preprocessing, federated model aggregation, predictive risk scoring, performance evaluation, and continuous model adaptation. Evaluation metrics include precision, recall, F1-score, false-positive rate, communication overhead, and convergence efficiency. Privacy-preserving mechanisms and secure aggregation are considered to reduce information disclosure during collaborative training. The proposed framework aims to improve threat detection responsiveness, strengthen cross-organizational security collaboration, and support evidence-based cybersecurity decisions. By integrating distributed intelligence with adaptive prediction, the research provides a foundation for scalable, privacy-conscious, and resilient enterprise cloud security management
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