Layered Model Stacking: Enhancing DDoS Detection Through Advanced Ensemble Machine Learning Techniques
Paper
Paper/Presentation Title | Layered Model Stacking: Enhancing DDoS Detection Through Advanced Ensemble Machine Learning Techniques |
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Presentation Type | Paper |
Authors | Haddad, Nabeel Mahdy, Sahi, Aqeel, Diykh, Mohammed, Aljbur, Kaled, Kutfan, Ali, Abdulla, Shahab and Al-Hraishawi, Hayder |
Editors | Chen, F. |
Journal or Proceedings Title | Proceedings of 2024 IEEE Region 10 Conference (TENCON 2024) |
ERA Conference ID | 61640 |
Number of Pages | 4 |
Year | 2025 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | IEEE |
ISBN | 9798350350821 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TENCON61640.2024.10902823 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/10902823 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/10902665/proceeding |
Conference/Event | 2024 IEEE Region 10 Conference (TENCON 2024) |
Event Details | 2024 IEEE Region 10 Conference (TENCON 2024) Parent TENCON Spring Conference Delivery In person Event Date 01 to end of 04 Dec 2024 Event Location Singapore Event Venue Sands Expo & Convention Centre Event Description IEEE Region 10 International Conference TENCON |
Abstract | 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. |
Keywords | Layered Model Stacking; DDoS; Ensemble Machine Learning Techniques |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4611. Machine learning |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Misan, Iraq |
School of Mathematics, Physics and Computing | |
Al-Shatrah University, Iraq | |
University of Ti-Qar, Iraq | |
Al-Ayen University, Iraq | |
TAFE Queensland | |
UniSQ College | |
University of Luxembourg |
https://research.usq.edu.au/item/zww58/layered-model-stacking-enhancing-ddos-detection-through-advanced-ensemble-machine-learning-techniques
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