An Adaptive Machine Learning Framework for Anomaly Detection in Cloud-Based Storage Systems

Abstract

Critical anomaly detection difficulties, such as false alarms during workload variations and delayed breach detection, have been brought about by the quick adoption of cloud-based storage systems. Data integrity and operational effectiveness are jeopardized by traditional static models' inability to adjust to the dynamic nature of cloud settings. In order to improve anomaly detection accuracy and resource optimization, this study created and verified an adaptive machine learning framework that makes use of real-time model updates and domain-specific cloud infrastructure information. CloudSim simulations of 1,000 cloudlets (10 runs, σ = 0.000), a quantitative survey of 51 IT specialists (92.7% response rate), and qualitative interviews with 13 infrastructure administrators were all included in the mixed-methods sequential explanatory design. A substantial importance-implementation gap in domain knowledge was found (Δ = 1.45, p <.001). With only 15% CPU overhead, the suggested framework, which is based on a domain-enhanced Random Forest, improved the F1-score by 64% and decreased false positives by 54% when compared to static thresholds. By bridging the gap between theoretical machine learning and the realities of cloud infrastructure in Africa, the study offers a deployable framework for improving cloud security in Kenya and other resource-constrained environments.