Machine Learning Models for DDoS Detection in Software-Defined Networking: A Comparative Analysis
Abstract
In today's digital age, Software-Defined Networking (SDN) has become a pivotal technology that improves network control and flexibility. Despite its advantages, the centralized nature of SDN also makes it susceptible to threats such as Distributed Denial of Service (DDoS) attacks. This study compares the effectiveness of three machine learning models Random Forest, Naive Bayes, and Linear Support Vector Classification (LinearSVC) using the 'DDoS SDN dataset' from Kaggle, which contains 104,345 records and 23 features. An equal 70/30 ratio was used on model. The models were then assessed using measures such as accuracy, precision, recall, and F1-score, and ROC curves. Among the models, Random Forest outperformed the others with a 97% accuracy, precision values of 1.00 (benign traffic) and 0.94 (malicious traffic), and an ROC AUC score of 1.00. In contrast, Naive Bayes and LinearSVC recorded lower accuracies of 63% and 66%, respectively. These findings underscore Random Forest's effectiveness in detecting DDoS attacks within SDN environments.
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References
K. Govindarajan, K. C. Meng, and H. Ong, “A literature review on software-defined networking (SDN) research topics, challenges and solutions,” in 2013 fifth International conference on advanced computing (ICoAC), 2013, pp. 293–299.
S. Siddiqui et al., “Toward software-defined networking-based IoT frameworks: A systematic literature review, taxonomy, open challenges and prospects,” IEEE Access, vol. 10, pp. 70850–70901, 2022.
A. Aljuhani, “Machine learning approaches for combating distributed denial of service attacks in modern networking environments,” IEEE Access, vol. 9, pp. 42236–42264, 2021.
T. Radivilova, L. Kirichenko, D. Ageiev, and V. Bulakh, “Classification methods of machine learning to detect DDoS attacks,” in 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2019, pp. 207–210.
U. Ince and G. Karaduman, “Classification of Distributed Denial of Service Attacks Using Machine Learning Methods,” NATURENGS, vol. 5, no. 1, pp. 15–20, 2024.
P. S. Saini, S. Behal, and S. Bhatia, “Detection of DDoS attacks using machine learning algorithms,” in 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom), 2020, pp. 16–21.
Y. Al-Hadhrami and F. K. Hussain, “DDoS attacks in IoT networks: a comprehensive systematic literature review,” World Wide Web, vol. 24, no. 3, pp. 971–1001, 2021.
A. A. Bahashwan, M. Anbar, S. Manickam, T. A. Al-Amiedy, M. A. Aladaileh, and I. H. Hasbullah, “A systematic literature review on machine learning and deep learning approaches for detecting DDoS attacks in software-defined networking,” Sensors, vol. 23, no. 9, p. 4441, 2023.
S. Kumar, N. P. Singh, and N. Kumar, “Literature Review of Distributed Denial of Service (DDoS) Attacks, its Detection Techniques and Prevention Mechanisms,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 10, pp. 1681–1685, 2022.
G. Somani, M. S. Gaur, D. Sanghi, M. Conti, and R. Buyya, “DDoS attacks in cloud computing: Issues, taxonomy, and future directions,” Comput. Commun., vol. 107, pp. 30–48, 2017.
S. K. Fayaz, Y. Tobioka, V. Sekar, and M. Bailey, “Bohatei: Flexible and elastic DDoS defense,” in 24th USENIX security symposium (USENIX Security 15), 2015, pp. 817–832.
T. Aytaç, M. AYDIN, and A. ZA.IM, “Detection DDOS attacks using machine learning methods,” Electrica, vol. 20, no. 2, 2020.
M. W. Nadeem, H. G. Goh, V. Ponnusamy, and Y. Aun, “DDoS Detection in SDN using Machine Learning Techniques.,” Comput. Mater. & Contin., vol. 71, no. 1, 2022.
J. Brownlee, “Bagging and random forest ensemble algorithms for machine learning,” Mach. Learn. Algorithms, pp. 4–22, 2016.
A. A. Abdulrahman and M. K. Ibrahem, “Evaluation of DDoS attacks detection in a CICIDS2017 dataset based on classification algorithms,” Iraqi J. Inf. Commun. Technol., vol. 1, no. 3, 2018.
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