Anomaly Detection in Cloud Computing: A Systematic Review of Machine Learning Approaches
Keywords:
Cloud Computing, Anomaly Detection, Machine Learning, Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Cloud Performance, System Reliability, Real-Time Monitoring, Cloud SecurityAbstract
Cloud computing has become an essential pillar of digital infrastructure, offering on-demand services with high scalability and flexibility. However, ensuring consistent performance and reliability in such dynamic environments remains a significant challenge. Anomaly detection plays a critical role in identifying deviations from normal behavior that could indicate system faults, performance bottlenecks, or security breaches. Recently, machine learning (ML) techniques have shown remarkable success in detecting such anomalies due to their ability to analyze large volumes of heterogeneous data and adapt to evolving patterns. This systematic review explores various ML-based anomaly detection methods applied within cloud computing environments. The paper categorizes the approaches into supervised, unsupervised, and semi-supervised models, examining their performance, scalability, real-time capabilities, and implementation complexity. Additionally, it highlights the challenges related to data labeling, model generalization, and integration into live cloud systems. Key publicly available datasets and evaluation metrics used in the literature are also reviewed. The study concludes by identifying research gaps and proposing future directions to enhance the robustness, interpretability, and efficiency of ML-driven anomaly detection frameworks in cloud settings.
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