Advancements in Machine Learning-Based DDoS Attack Detection within Software-Defined Networking Environments

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.

• Periodical: Engineering Headway (Volume 35)
• Pages: 243-253
• DOI: https://doi.org/10.4028/p-MU7mPL
• Online since: February 2026
• Authors: Karrar E. Alhamami, Hashim Ali, Fahad G. Abdulkadhim, Riyadh Gafel, Ali Ayad Kdhm
• Keywords: DDoS Attack, Decision Tree, K-Nearest Neighbors, Logistic Regression, ML, Native Bayes, Random Forest, SDN, Support Vector Machine (SVM), XGBoost

References

[1] Aditya, T., Donald, A. D., Thippanna, G., Kousar, M. M., & Swetha, K. (2023). The Future of Networking: Embracing Software-Defined Solutions. Future, 3(2).

DOI: 10.48175/ijarsct-8528

[2] Alshaya, A. (2023). Software-Defined Networking Security Techniques and the Digital Forensics of the SDN Control Plane.

DOI: 10.31390/gradschool_dissertations.6143

[3] Nunez, A., Ayoka, J., Islam, M. Z., & Ruiz, P. (2023). A Brief Overview of Software-Defined Networking. arXiv preprint arXiv:2302.00165.

[4] Badotra, S., Tanwar, S., Bharany, S., Rehman, A. U., Eldin, E. T., Ghamry, N. A., & Shafiq, M. (2022). A DDoS Vulnerability Analysis System against Distributed SDN Controllers in a Cloud Computing Environment. Electronics, 11(19), 3120.

DOI: 10.3390/electronics11193120

[5] El Sayed, M. S., Le-Khac, N. A., Azer, M. A., & Jurcut, A. D. (2022). A flow-based anomaly detection approach with feature selection method against ddos attacks in sdns. IEEE Transactions on Cognitive Communications and Networking, 8(4), 1862-1880.

DOI: 10.1109/tccn.2022.3186331

[6] Bhayo, J., Shah, S. A., Hameed, S., Ahmed, A., Nasir, J., & Draheim, D. (2023). Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks. Engineering Applications of Artificial Intelligence, 123, 106432.

DOI: 10.1016/j.engappai.2023.106432

[7] Alheeti, K. M. A., Alzahrani, A., Alamri, M., Kareem, A. K., & Al_Dosary, D. (2023). A Comparative Study for SDN Security Based on Machine Learning. International Journal of Interactive Mobile Technologies, 17(11).

DOI: 10.3991/ijim.v17i11.39065

[8] Liu, Z., Wang, Y., Feng, F., Liu, Y., Li, Z., & Shan, Y. (2023). A DDoS Detection Method Based on Feature Engineering and Machine Learning in Software-Defined Networks. Sensors, 23(13), 6176.

DOI: 10.3390/s23136176

[9] Mansoor, A., Anbar, M., Bahashwan, A. A., Alabsi, B. A., & Rihan, S. D. A. (2023). Deep Learning-Based Approach for Detecting DDoS Attack on Software-Defined Networking Controller. Systems, 11(6), 296.

DOI: 10.3390/systems11060296

[10] Demertzi, V., Demertzis, S., & Demertzis, K. (2023). An Overview of Cyber Threats, Attacks and Countermeasures on the Primary Domains of Smart Cities. Applied Sciences, 13(2), 790.

DOI: 10.3390/app13020790

[11] Bugaje, A. A. B., Cremer, J. L., & Strbac, G. (2023). Split-based sequential sampling for realtime security assessment. International Journal of Electrical Power & Energy Systems, 146, 108790.

DOI: 10.1016/j.ijepes.2022.108790

[12] Chindove, H., & Brown, D. (2021, December). Adaptive machine learning based network intrusion detection. In Proceedings of the International Conference on Artificial Intelligence and its Applications (pp.1-6).

DOI: 10.1145/3487923.3487938

[13] Hossain, M. A., & Islam, M. S. (2023). Ensuring network security with a robust intrusion detection system using ensemble-based machine learning. Array, 19, 100306.

DOI: 10.1016/j.array.2023.100306

[14] Liu, J. (2023, April). Research on Computer Network Secure Communication and Encryption Algorithm Based on Machine Learning. In 2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI) (pp.113-117). IEEE.

DOI: 10.1109/acedpi58926.2023.00030

[15] Jovanovic, L., Jovanovic, D., Antonijevic, M., Zivkovic, M., Budimirovic, N., Strumberger, I., & Bacanin, N. (2022, September). The XGBoost Tuning by Improved Firefly Algorithm for Network Intrusion Detection. In 2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) (pp.268-275). IEEE.

DOI: 10.1109/synasc57785.2022.00050

[16] Jundi, Z. Z., & Alyasiri, H. (2023, July). Android Malware Detection Based on Grammatical Evaluation Algorithm and XGBoost. In 2023 Al-Sadiq International Conference on Communication and Information Technology (AICCIT) (pp.70-75). IEEE.

DOI: 10.1109/aiccit57614.2023.10217965

[17] Arunkumar, J. R., Velmurugan, S., Chinnaiah, B., Charulatha, G., Prabhu, M. R., & Chakkaravarthy, A. P. (2023). Logistic Regression with Elliptical Curve Cryptography to Establish Secure IoT. Computer Systems Science & Engineering, 46(1).

DOI: 10.32604/csse.2023.031605

[18] Hosseinzadeh, M., Rahmani, A. M., Vo, B., Bidaki, M., Masdari, M., & Zangakani, M. (2021). Improving security using SVM-based anomaly detection: issues and challenges. Soft Computing, 25, 3195-3223.

DOI: 10.1007/s00500-020-05373-x

[19] Osho, O., & Hong, S. (2021). An Overview: Stochastic Gradient Descent Classifier, Linear Discriminant Analysis, Deep Learning and Naive Bayes Classifier Approaches to Network Intrusion Detection. International Journal of Engineering and Technical Research, 10(4), 294-308.

[20] Yang, H., Liang, S., Ni, J., Li, H., & Shen, X. S. (2020). Secure and efficient k NN classification for industrial Internet of Things. IEEE Internet of Things Journal, 7(11), 10945-10954.

DOI: 10.1109/jiot.2020.2992349

[21] Douiba, M., Benkirane, S., Guezzaz, A., & Azrour, M. (2023). Anomaly detection model based on gradient boosting and decision tree for IoT environments security. Journal of Reliable Intelligent Environments, 9(4), 421-432.

DOI: 10.1007/s40860-022-00184-3

[22] Choubisa, M., Doshi, R., Khatri, N., & Hiran, K. K. (2022, May). A simple and robust approach of random forest for intrusion detection system in cyber security. In 2022 International Conference on IoT and Blockchain Technology (ICIBT) (pp.1-5). IEEE.

DOI: 10.1109/icibt52874.2022.9807766

[23] Folz, F., Mehlhorn, K., & Morigi, G. (2023). Noise-induced network topologies. Physical Review Letters, 130(26), 267401.

DOI: 10.1103/physrevlett.130.267401

[24] Arias, J., Knapik, M., Penczek, W., & Petrucci, L. (2022, October). Modular Analysis of Tree-Topology Models. In International Conference on Formal Engineering Methods (pp.36-53). Cham: Springer International Publishing.

DOI: 10.1007/978-3-031-17244-1_3

[25] Chen, K. T., Ngo, Q. Q., Kurzhals, K., Marriott, K., Dwyer, T., Sedlmair, M., & Weiskopf, D. (2023). Reading Strategies for Graph Visualizations that Wrap Around in Torus Topology. arXiv preprint arXiv:2303.17066.

DOI: 10.1145/3588015.3589841