Cybersecurity Aware Machine Learning Framework for Real Time Adaptive Threat Detection in Distributed Cloud Environments

Abstract

The rapid adoption of distributed cloud environments has significantly increased enterprise scalability, flexibility, and accessibility while simultaneously expanding the cybersecurity attack surface. Conventional security mechanisms based on static rules and predefined signatures are increasingly inadequate for identifying sophisticated, evolving, and previously unseen cyber threats. This research proposes a Cybersecurity Aware Machine Learning Framework for real-time adaptive threat detection across distributed cloud infrastructures. The framework integrates machine learning-based behavioral analytics, continuous telemetry collection, contextual risk assessment, adaptive anomaly detection, and automated response mechanisms to identify malicious activities across virtual machines, containers, APIs, applications, networks, and cloud services. A cybersecurity-aware learning architecture is designed to incorporate security context into feature engineering, model training, threat classification, and response prioritization. The proposed methodology combines supervised and unsupervised learning with adaptive model updating to address concept drift and emerging attack patterns. Distributed monitoring agents collect security and operational data, while a centralized intelligence layer performs correlation, prediction, and risk scoring. Experimental evaluation can be conducted using accuracy, precision, recall, F1-score, false-positive rate, detection latency, resource utilization, and adaptation performance. The framework aims to improve real-time detection accuracy, reduce response delays, strengthen resilience against evolving threats, and provide scalable security intelligence for heterogeneous distributed cloud environments.