XGBoost-Based Intelligent Intrusion Detection for Secure Cloud Computing Using Correlation-Based Feature Selection

Cloud Computing has emerged as an essential platform for the modern applications. But the growing complexity of the attacks puts pressure on the security of cloud environments. Current intrusion detection systems (IDS) have weaknesses of detection inaccuracy, high false alarm rate and low efficiency in processing high-dimensional network traffic data. To overcome these drawbacks, this study suggests an intelligent intrusion detection framework based on XGBoost algorithm with correlation-based feature selection to remove redundant features, decrease the computational complexity, and enhance the classification performance. The proposed framework is based on UNSW-NB15 dataset, which consists of realistic normal and malicious network traffic, and data preprocessing methods such as categorical encoding, feature scaling, and a 70:30 ratio for training and testing. The results of this experimental study show that the proposed model attains 99.11% accuracy, 99.13% precision, 99.57% recall, 99.35% F1 score and 99.84% AUC which is higher than that achieved by the traditional machine learning models like random forest, decision tree, naïve bayes etc. The results show that the proposed framework is accurate, efficient and scalable solution for intelligent cloud intrusion detection which improves the security and reliability of cloud computing environments.