Advancing Cloud Anomaly Detection: A Machine Learning Approach

Cloud computing has transformed modern IT architecture by enabling scalable and flexible computing resources. As the popularity of cloud-based services evolves, so do security concerns, making anomaly detection an important field of research. The purpose of this paper is to provide a comprehensive review of cloud anomaly detection methods, with a particular emphasis on machine learning-based approaches. In order to validate the performance of machine learning over anomaly detection on cloud, ten different machine learning algorithms are applied on three datasets. Accuracy, precision, recall, and F1-score are calculated. Out of these ten techniques AdaboostM1 performs best in dataset 1 with values of 0.965449, 0.945588, 0.950879 and 0.945588 for precision, recall, F1-Score and accuracy respectively. While decision tree, hoeffeding tree and $\mathbf{J 4 8}$ performed best with same values in dataset $\mathbf{2}$ in terms of all measurement metrics with accuracy 0.997929, precision 0.997987, recall 0.997929 and F1-Score 0.997946. In dataset 3 AdaboostM1, NaiveBayes and VotedPerceptron performed best in terms of recall, F1-score, and accuracy with values 0.831081, 0.754413 and 0.831081 respectively, while in terms of precision decision tree, hoeffeding tree and J48 performed best with same value of 0.711735. The research reveals that different techniques are well suited in cloud security on different datasets and creates opportunities for future scalable and adaptable anomaly detection systems.