Layered Model Stacking: Enhancing DDoS Detection Through Advanced Ensemble Machine Learning Techniques

Distributed Denial of Service (DDoS) attacks continue to cause a substantial threat to network infrastructure and services. In this paper, we propose an approach called DDoS Layered Model Stacking (DDoS_LMS) to improve DDoS detection accuracy. Our model uses advanced ensemble machine-learning techniques to enhance the robustness and reliability of detection systems. We evaluate our model using a dataset of network traffic, including both legitimate and attack traffic. Multiple machine learning models are employed, such as Logistic Regression, k-nearest Neighbors (k-NN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Naive Bayes. Our proposed model, which combines the strengths of these individual classifiers, achieves exceptional results with 0.9872 accuracy, 0.9829 precision, 0.9847 recall, and 0.9837 F1 score. The DDoS_LMS notably outperforms individual models and proves its efficiency in detecting DDoS attacks.