Predictive Threat Intelligence Analytics Frameworks using Machine Learning for Secure Cloud-Native Enterprise Platforms

Abstract

The rapid adoption of cloud-native architectures, containerized applications, microservices, serverless computing, and distributed data platforms has significantly expanded the attack surface of modern enterprises. Traditional cybersecurity approaches that rely primarily on signature-based detection and reactive incident response are increasingly inadequate for identifying sophisticated, rapidly evolving, and previously unknown threats. Predictive threat intelligence analytics supported by machine learning (ML) offers an alternative approach by analyzing large volumes of heterogeneous security data to identify behavioral patterns, anomalies, emerging attack indicators, and potential future threats. This paper proposes a research framework for developing machine-learning-based predictive threat intelligence analytics for secure cloud-native enterprise platforms. The framework integrates security telemetry from cloud infrastructure, application workloads, identity systems, network traffic, containers, endpoints, and threat-intelligence feeds. Data preprocessing, feature engineering, supervised and unsupervised learning, temporal analysis, anomaly detection, and risk prediction are combined to generate actionable intelligence. The methodology emphasizes model accuracy, scalability, explainability, robustness, and operational usefulness while considering challenges such as concept drift, imbalanced datasets, false positives, adversarial manipulation, privacy, and heterogeneous cloud environments. Experimental evaluation can compare machine-learning models using detection, prediction, computational, and operational metrics. The proposed approach aims to support proactive security monitoring, earlier threat identification, improved incident prioritization, and adaptive defense mechanisms across complex cloud-native enterprise environments.