Towards Effective Anomaly Detection: Machine Learning Solutions in Cloud Computing

Cloud computing has transformed modern Information Technology (IT) infrastructures with its scalability and cost-effectiveness but introduces significant security risks. More-over, existing anomaly detection techniques are not well equipped to deal with the complexities of dynamic cloud environments. This systematic literature review shows the advancements in Machine Learning (ML) solutions for anomaly detection in cloud computing. The study categorizes ML approaches, examines the datasets and evaluation metrics utilized, and discusses their effectiveness and limitations. We analyze supervised, unsupervised, and hybrid ML models showing their advantages in dealing with a certain threat vector. It also discusses how advanced feature engineering, ensemble learning and real-time adaptability can improve detection accuracy and reduce false positives. Some key challenges, such as dataset diversity and computational efficiency, are highlighted, along with future research directions to improve ML based anomaly detection for robust and adaptive cloud security. Hybrid approaches are found to increase the accuracy reaching up to 99.85% and reduces the number of false positives. This review provides a comprehensive guide to researchers aiming to enhance anomaly detection in cloud environments.

@article{Almajed2025,
  title     = {Towards Effective Anomaly Detection: Machine Learning Solutions in Cloud Computing},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Hussain Almajed and Abdulrahman Alsaqer and Abdullah Albuali},
  doi       = {10.14569/IJACSA.2025.01602132},
  url       = {https://doi.org/10.14569/IJACSA.2025.01602132}
}