Description of Invention
This study introduces a hybrid ensemble machine learning model that combines Gradient Boosting (GB) and Extreme Gradient Boosting (XGBoost) with Explainable AI (XAI) techniques to improve DDoS attack detection. The model addresses challenges faced by traditional Intrusion Detection Systems (IDS) in detecting complex DDoS attacks. By integrating SHapley Additive exPlanations (SHAP), it enhances detection accuracy and provides transparent insights into attack identification. After thorough data pre-processing and feature selection, the model achieves 99.96% accuracy and 100% precision, outperforming conventional algorithms by reducing false positives and negatives, making it ideal for real-time cybersecurity applications.