This study introduces an intelligent approach to boost detection in cloud environments, specifically focusing on improving detection rates. The proposed technique implements a detection system using the support vector machine (SVM) classifier. The model consists of three sequential phases: dataset collection, encoding, and training. The encoding stage converts all symbols and letters in the dataset to numerical values, which is essential for the functioning of the SVM. In the final phase, the SVM classifier is trained on the dataset. To evaluate the proposed approach, a testing phase was employed to determine the accuracy of detection. Assessment metrics such as accuracy, precision, recall, and F1-score were computed. The findings demonstrate that the suggested technique is highly effective in achieving accurate detection, indicating its potential application in cloud-based anomaly detection.