This research presents a novel adaptive threat detection framework that leverages lightweight hybrid learning to enhance cybersecurity in cloud-scale environments. Addressing the limitations of traditional intrusion detection systems and standalone machine learning models, the proposed approach integrates supervised and unsupervised learning techniques within a resource-efficient, scalable architecture. The model is designed to detect both known and unknown threats by combining classification capabilities with anomaly detection, further strengthened by a continuous feedback loop for real-time adaptability. Experiments conducted using benchmark datasets such as CICIDS2017 and UNSW-NB15, along with simulated cloud traffic, demonstrate that the proposed system outperforms existing solutions in terms of accuracy, precision, recall, F1-score, and AUC, while maintaining low latency and high scalability. Deployed within a containerized environment to emulate real-world conditions, the model showcases excellent performance in handling dynamic workloads, evolving attack patterns, and compliance-sensitive deployments. This study establishes a practical, efficient, and future-ready framework for intelligent threat detection, contributing significantly to the advancement of secure cloud computing.
Adaptive threat detection, hybrid learning, cloud security, machine learning, anomaly detection
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