Enhanced Multi-Class DDoS Attack Identification using a Meta-Learning Ensemble

Abstract

Distributed Denial of Service (DDoS) attacks continue to pose significant threats to network availability and security. While many detection systems focus on binary classification (attack vs. benign), effective mitigation often requires identifying the specific type of DDoS attack. This paper introduces a robust intrusion detection framework centered around a high-accuracy, multi-class classification model designed to precisely identify various DDoS attack types. We propose an ensemble architecture integrating Long Short-Term Memory (LSTM), K-Nearest Neighbors (KNN), and Random Forest (RF) models, whose outputs are synthesized by a Logistic Regression meta-learner. This approach explicitly addresses the ambiguity often encountered when combining predictions from multiple independent classifiers. Evaluated on the CIC-DDoS2019 dataset, our proposed ensemble meta-learning model achieves 96% accuracy in the multi-class identification task, significantly outperforming a baseline chain model (combining individual binary classifiers), which reached 92% accuracy and suffered from high ambiguity. Furthermore, integration and testing within a Software-Defined Networking (SDN) environment using Mininet and the Ryu controller demonstrated the practical applicability of our model, achieving 93% accuracy in identifying DDoS types in the emulated network traffic. Our work highlights the value of meta-learning ensembles for nuanced DDoS threat identification, paving the way for more adaptive and effective defense mechanisms.