Enhancing intrusion detection in cloud environments through ensemble learning and feature selection techniques

With the rapid adoption of cloud computing, securing cloud environments against cyber threats has become a critical challenge. Intrusion Detection Systems (IDS) play a pivotal role in identifying malicious activities, but traditional methods often struggle with the high dimensionality of data and evolving attack patterns in cloud ecosystems. This research proposes a novel approach to improve intrusion detection by leveraging ensemble learning and feature selection techniques. Ensemble learning combines multiple machine learning models to enhance detection accuracy and robustness, while feature selection reduces data dimensionality, improving computational efficiency and model performance. The study evaluates various ensemble methods, such as Random Forest, Gradient Boosting, and Stacking, alongside feature selection algorithms like Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA). Experiments are conducted on benchmark datasets, such as CICIDS2017 and NSL-KDD, to assess the effectiveness of the proposed framework. Results demonstrate that the integration of ensemble learning and feature selection significantly improves detection rates, reduces false positives, and enhances the scalability of IDS in cloud environments. This research contributes to advancing cloud security by providing a robust and efficient intrusion detection framework.