An Enhanced Network Traffic Classification in Software-Defined Networking Using Softmax Regression
DOI:
https://doi.org/10.22401/anjs.29.3.3444Keywords:
Software-Defined Networking, Network Traffic Classification, Traffic Management, Softmax RegressionAbstract
Network traffic classification in Software-Defined Networking is an essential aspect of SDN and plays an important role in the efficient management and security of modern networks. The classical algorithm for traffic classification based on port numbers cannot handle the increasing encrypted traffic and dynamic port allocation. At the same time, algorithms based on machine learning, for example, flow-based and statistics-based, fail to classify accurately because of the complexity of the multi-class problem. A multi-class traffic classification framework based on the Softmax Regression algorithm is proposed to efficiently identify DNS, Telnet, Ping, and Voice flows in an SDN environment. The algorithm provides a probabilistic method for multi-class classification maitaining both interpretability and high accuracy. Normalization and parameter tuning are carried out to enhance performance of classification. The results of the experiments show that the classification accuracy is 99.68% for the Softmax regression algorithm, while it is 98.53% for the logistic regression algorithm. This illustrates the significance of Softmax regression for multi-class classification and highlights its scalability and reliability for optimizing traffic management in dynamic SDN systems.
References
[1] Aldaoud, M.; Al-Abri, D.; Awadalla, M.; Kausar, F.; "Leveraging ICN and SDN for Future Internet Architecture: A Survey". Electronics, 12 (7): 1723, 2023.
[2] Ferreira, G.O.; Ravazzi, C.; Dabbene, F.; Calafiore, G.C.; Fiore, M.; "Forecasting Network Traffic: A Survey and Tutorial with Open-Source Comparative Evaluation". IEEE Access, 11 (1): 6018–6044, 2023.
[3] Azab, A.; Khasawneh, M.; Alrabaee, S.; Choo, K.K.R.; Sarsour, M.; "Network Traffic Classification: Techniques, Datasets, and Challenges". Digit. Commun. Netw., 10 (3): 676–692, 2024.
[4] Serag, R.H.; Abdalzaher, M.S.; Elsayed, H.A.E.A.; Sobh, M.; Krichen, M.; Salim, M.M.; "Machine-Learning-Based Traffic Classification in Software-Defined Networks". Electronics, 13 (6): 1108, 2024.
[5] Abdi, A.H.; Audah, L.; Salh, A.; Alhartomi, M.A.; Rasheed, H.; Ahmed, S.; Tahir, A.; et al.; "Security Control and Data Planes of SDN: A Comprehensive Review of Traditional, AI and MTD Approaches to Security Solutions". IEEE Access, 12: 69941-69980, 2024.
[6] Sokolov, Y.A.; Pugach, A.V.; Baykalova, N.A.; Gaipov, K.E.; Knyazeva, I.O.; Patsel, D.E.; Lukevich, D.R.; et al.; "Computational Analysis of the Influence of Internet Traffic Flows in IP Networks". In: AIP Conf. Proc., Melville, NY, USA, 12 January 2024; Igor Kovalev and Anna Voroshilova., Eds.; AIP Publishing; 2024; AIP Publishing: Melville, NY, USA, 2024.
[7] Shoba, V.B.T.; Sam, I.S.; "Adaptive Deep Feature Learning-Based Softmax Regressive Classification for Aging Facial Recognition". Multimed. Tools Appl., 82 (15): 22343–22371, 2023.
[8] Salau, A.O.; Beyene, M.M.; "Software-Defined Networking-Based Network Traffic Classification Using Machine Learning Techniques". Sci. Rep., 14 (1): 20060, 2024.
[9] Adil, S.; Alfoudi, A.S.; "Traffic Data Classification in SDN Network Based on Machine Learning Algorithms". Wasit J. Pure Sci., 3 (2): 161–171, 2024.
[10] Ashour, M.M.; Yakout, M.A.; AbdElhalim, E.; "Traffic Classification in Software-Defined Networks Based on Machine Learning Algorithms". Int. J. Telecommun., 4 (1): 1–19, 2024.
[11] Wang, Z.; Li, Z.; Fu, M.; Ye, Y.; Wang, P.; "Network Traffic Classification Based on Federated Semi-Supervised Learning". J. Syst. Archit., 149 (1): 103091, 2024.
[12] Eldhai, A.M.; Hamdan, M.; Abdelaziz, A.; Hashem, I.; Marsono, M.N.; Hamzah, M.; Jhanjhi, N.Z.; "Improved Feature Selection and Stream Traffic Classification Based on Machine Learning in Software-Defined Networks". IEEE Access, 12: 34141-34159, 2024.
[13] Yoo, Y.; Yang, G.; Shin, C.; Lee, J.; Yoo, C.; "Machine Learning-Based Prediction Models for Control Traffic in SDN Systems". IEEE Trans. Serv. Comput., 16 (6): 4389-4403,2023.
[14] Belkadi, O.; Vulpe, A.; Laaziz, Y.; Halunga, S.; "ML-Based Traffic Classification in an SDN-Enabled Cloud Environment". Electronics, 12 (2): 269, 2023.
[15] Tonkal, Ö.; Polat, H.; "Traffic Classification and Comparative Analysis with Machine Learning Algorithms in Software-Defined Networks". Gazi Univ. J. Sci. Part C, 9 (1): 71–83, 2021.
[16] Raikar, M.M.; Meena, S.M.; Mulla, M.M.; Shetti, N.S.; Karanandi, M.; "Data Traffic Classification in Software-Defined Networks Using Supervised Learning". Procedia Comput. Sci., 171 (1): 2750–2759, 2020.
[17] Margariti, S.V.; Tsoulos, I.G.; Kiousi, E.; Stergiou, E.; "Traffic Classification in Software-Defined Networking Using Genetic Programming Tools". Future Internet, 16 (9): 338, 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Prabu U, Gorli Uday Kumar, Yadlapalli Leelakrishna, Pagadam Siddu, Geetha V
This work is licensed under a Creative Commons Attribution 4.0 International License.
.jpg)
