| Title |
DDoS Attack Detection in Software Defined Networking Using Machine Learning Techniques |
| Authors |
(Fazeela Mughal) ; (Ghulam Musa Raza) ; (Muhammad Waseem Raza) ; (Byung-Seo Kim) |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.5.701 |
| Keywords |
SDN; DDoS; Machine learning; Security; Features; Dataset |
| Abstract |
Machine learning techniques enhance security in Software-Defined Networks by detecting attacks.
Among various network threats, Distributed Denial of Service (DDoS) attacks are the most prevalent, causing monetary loss and server outages. Extensive research has focused on detecting these attacks using real-time and synthetic datasets. This paper evaluates the performance of machine learning algorithms in detecting DDoS attacks with high accuracy. A simulated dataset was generated using Packet Sender, and five benchmark datasets were selected: three real-time datasets (CICDDOS2019, BoT-IoT, and ToN-IoT) and two synthetic datasets (Mendeley and Bogazici University DDoS Dataset). Seven machine learning algorithms?KNN, Decision Tree, Random Forest, Logistic Regression, MPL, XGBoost, and SVM?were trained and tested. Results are based on accuracy, considering the best features within each dataset instead of direct feature comparison across datasets. Since each dataset has distinct features, using identical features for all datasets may yield biased results. Some features may be highly relevant in one dataset but less significant in another. This study highlights the importance of selecting optimal dataset-specific features to improve attack detection accuracy while mitigating bias in comparative analysis. |