Abstract
The study proposes an enhanced cloud intrusion detection system (IDS) that tackles security challenges in cloud computing, focusing on data imbalance and feature selection. By integrating SMOTE for data imbalance and a hybrid feature selection method, the system achieves exceptional accuracies exceeding 98% and 99% on two datasets. The use of fewer informative features enhances system efficiency, showcasing its practical applicability and effectiveness in real-world scenarios. Overall, the study contributes significantly to cloud security by offering a holistic approach to IDS enhancement
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