Advancements in Machine Learning-Based DDoS Attack Detection within Software-Defined Networking Environments
الباحث الأول:
Karrar E. Alhamami
الباحثين الآخرين:
Hashim Ali2, Fahad G. Abdulkadhim, Riyadh Gafel, Ali Ayad Khdm
المجلة:
Engineering Headway
تاريخ النشر:
23 فبراير، 2026
مختصر البحث:
Abstract. Software-defined networking (SDN) infrastructures are facing a growing menace from cyber threats, which leave them vulnerable to distributed denial of service (DDoS) attacks. This study extends the earlier research [Detecting DDOS attacks …
Abstract. Software-defined networking (SDN) infrastructures are facing a growing menace from cyber threats, which leave them vulnerable to distributed denial of service (DDoS) attacks. This study extends the earlier research [Detecting DDOS attacks in SDN Networks Using Machine Learning Techniques], which extensively investigated vulnerabilities in the SDN architecture for DDoS attacks. We develop and evaluate machine learning methods that are specifically tailored to protect software-defined networks (SDN) from such malicious attacks. The centralized and rigorous operational protocol of SDN has enabled us to develop a range of detecting methods. This study examines the efficacy of different machine learning algorithms, including XGBoost, Native Bayes, Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forests, and K-Nearest Neighbors (KNN). The algorithm's computational efficiency and precision were evaluated using a dataset specifically created to simulate the intricate and unpredictable characteristics of network traffic. The experimental findings demonstrate that the XGBoost and Random Forest algorithms exhibit commendable performance in terms of both accuracy and speed. The precision varies between 99.26% and 77.0%, depending on the specific detection algorithm employed and the selected features. Consequently, these algorithms are well-suited for promptly dealing with and reducing potential hazards. XGBoost demonstrated exceptional versatility by maintaining accuracy across several testing scenarios while also reaching great processing efficiency. Utilizing machine learning has the potential to significantly enhance the security of SDN systems, as indicated by the results. This revelation has two significant ramifications: This study enhances our comprehension of efficient SDN DDoS mitigation strategies and sets a standard for the next research on integrating machine learning in security frameworks. We offer essential knowledge that aids in preserving vital network infrastructures in an ever more unpredictable digital landscape.
