Pendekatan Ensemble Learning untuk klasifikasi serangan DDoS

Muhammad Oriza Nurfajri, Guntur Budi Herwanto — Sat, 19 Jul 2025 00:00:00 +0000

This research proposes an ensemble learning approach for classifying Distributed Denial of Service (DDoS) attacks using the CIC-DDoS2019 dataset. DDoS attacks remain a significant threat to network security, necessitating efficient detection methods. We developed an ensemble model combining Random Forest, Gradient Boosting, and AdaBoost classifiers to enhance detection accuracy. Our methodology involves preprocessing the CIC-DDoS2019 dataset, extracting relevant features, and implementing both binary classification (benign vs. attack) and multiclass classification (attack type identification). The experimental results show that our ensemble model achieves an F1-score of 0.9967 for binary classification, with Gradient Boosting performing best among individual models. The multiclass classification reaches an accuracy of 0.8742 in distinguishing between different types of DDoS attacks. This research demonstrates that ensemble learning significantly improves the accuracy and reliability of DDoS attack detection compared to single-model approaches.

Keywords: Ensemble learning; DDoS attack; network security; CIC-DDoS2019; machine learning.

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Pendekatan Ensemble Learning untuk klasifikasi serangan DDoS