The Cloud computing is scalable and ondemand service to users; its open and distributed structure makes it a prime target for Distributed Denial of Service (DDoS) attacks. These attacks affect the availability of cloud services and pose serious security risks. A hybrid DDoS detection model based on XGBoost algorithm and Chi-Square feature selection technique is presented. The ChiSquare method is used to statistically select important network traffic features. the data dimension and increasing the interpretability of the model. Normal and malicious traffic is then classified using the XGBoost classifier. The model analysis is based on the standard datasets including NSL-KDD and CICIDS2017. the fundamental key performance metrics such as accuracy, recall, precision, F1- score, and ROC-AUC. Its fast processing and low-key alarm rate, the model for real-time attack detection in cloud environments.
DDoS, Detection Cloud Security, XGBoost Classifier, Chi-Square Feature Selection, Machine Learning, Intrusion Detection System (IDS), NSL-KDD Dataset, CICIDS2017 Dataset Network Traffic, Classification Real-time Threat Detection