Electronic and Cyber Defense

Electronic and Cyber Defense

Computer Networks traffic classification model based on DBScan clustering and gamma classification

Document Type : Original Article

Authors
Seyede Zohreh Majidian 1
Shiva TaghipourEivazi 2
Bahman Arasteh 3
Ali Ghaffari 4
1 PhD student. Department of Computer Engineering, Aras international Branch, Islamic Azad University, Tabriz, Iran
2 assistant professor. Department of Computer Engineering, Ta.C.،Islamic Azad university, Tabriz, Iran
3 Associate Professor. Department of Computer Engineering, Ta.C.،Islamic Azad university, Tabriz, Iran
4 Professor. Department of Computer Engineering, Ta.C.،Islamic Azad university, Tabriz, Iran
Abstract
Traffic classification is a crucial network monitoring process with wide applications in security, quality of service, and network management. With the increasing complexity and variety of network traffic, new challenges arise, including the lack of labeled training data. To address this challenge, this paper presents a traffic classification mechanism that combines unsupervised and semi-supervised machine learning algorithms. This mechanism uses a limited set of labeled training data to improve classification accuracy. The proposed method represents each traffic flow as a feature vector containing the statistical characteristics of that flow. The number of features generated for each sample is reduced using principal component analysis. DBScan clustering is employed to determine the correct traffic type for each untagged traffic stream. Finally, the gamma classifier model is used to separate the new traffic flows. The efficiency of the proposed method has been evaluated using real data sets. The results show that the proposed method can classify traffic flows with an average accuracy of 95.12%, representing at least a 7.03% improvement over previous approaches.
Keywords
Traffic classification machine learning DBScan clustering gamma classification ;">
Subjects

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[1]   M. Bazooband and H. Bahramghiri, “A New Hybrid Approach for Traffic Identification and Classi- fication in Wireless Networks,” Sci. J. Electron. Cyber Def., vol. 10, no. 2, 2022.(In Persian). dor: 20.1001.1.23224347.1401.10.2.4.0
[2]   S. Z. Majidian, “Reducing the Destructive Effect of Misbehaving Users in Cooperative Spectrum Sensing using Reinforcement Learning,” Sci. J. Electron. Cyber Def., vol. 10, no. 4, 2023. (In Persian).dor: 20.1001.1.23224347.1401.10.4.1.1
[3]   M. Cotton, L. Eggert, J. Touch, M. Westerlund, and S. Cheshire, “RFC 6335: Internet Assigned Numbers Authority (IANA) Procedures for the Management of the Service Name and Transport Protocol Port Number Registry.” RFC Editor, USA, 2011. doi: 10.17487/RFC6335
[4]   C. Defense, “Fault Proness Estimation of Software Modules Using Machine learning,” Sci. J. Electron. Cyber Def., vol. 11, no. 4, 2024.(In persion). dor: 20.1001.1.23224347.1402.11.4.4.1
[5]   E. Kazemi and A. Taei, “Presenting a New Sentiment Analysis Method Based on Multi-objective Archimedes Optimization Algorithm and Machine Learning,” Sci. J. Electron. Cyber Def., vol. 11, no. 4, 2024.  (In persion). dor: 20.1001.1.23224347.1402.11.4.12.9
[6]   A. J. R. K. Dadashtabar Ahmadi, M. Kheirkhah, “Detection of advanced Cyber Attacks, Using Behavior Modeling Based on Natural Language Processing,” J. Electron. Cyber Def., vol. 3, pp. 141–151, 2018. (In persion). dor:  20.1001.1.23224347.1397.6.3.12.2
[7]   G. Aceto, D. Ciuonzo, A. Montieri, and A. Pescapé, “Mobile Encrypted Traffic Classification Using Deep Learning,” in 2018 Network Traffic Measurement and Analysis Conference (TMA), 2018, pp. 1–8. doi: 10.23919/TMA.2018.8506558.
[8]   J. Yan, “A Survey of Traffic Classification Validation and Ground Truth Collection,” in 2018 8th International Conference on Electronics Information and Emergency Communication (ICEIEC), 2018, pp. 255–259. doi: 10.1109/ICEIEC.2018.8473477.
[9]   F. Pacheco, E. Exposito, M. Gineste, C. Baudoin, and J. Aguilar, “Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey,” IEEE Commun. Surv. Tutorials, vol. 21, no. 2, pp. 1988–2014, 2019, doi: 10.1109/COMST.2018.2883147.
[10] M. Shen, Y. Liu, L. Zhu, K. Xu, X. Du, and N. Guizani, “Optimizing Feature Selection for Efficient Encrypted Traffic Classification: A Systematic Approach,” IEEE Netw., vol. 34, no. 4, pp. 20–27, 2020, doi: 10.1109/MNET.011.1900366.
[11] S. Dong, “Multi class SVM algorithm with active learning for network traffic classification,” Expert Syst. Appl., vol. 176, p. 114885, 2021, doi: 10.1016/j.eswa.2021.114885.
[12] Z. Bu, B. Zhou, P. Cheng, K. Zhang, and Z.-H. Ling, “Encrypted Network Traffic Classification Using Deep and Parallel Network-in-Network Models,” IEEE Access, vol. 8, pp. 132950–132959, 2020, doi: 10.1109/ACCESS.2020.3010637.
[13] A. A. Afuwape, Y. Xu, J. H. Anajemba, and G. Srivastava, “Performance evaluation of secured network traffic classification using a machine learning approach,” Comput. Stand. Interfaces, vol. 78, p. 103545, 2021, doi: 10.1016/j.csi.2021.103545.
[14] G. D’Angelo and F. Palmieri, “Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction,” J. Netw. Comput. Appl., vol. 173, p. 102890, 2021, doi: 10.1016/j.jnca.2020.102890.
[15] R. Kumar, M. Swarnkar, G. Singal, and N. Kumar, “IoT Network Traffic Classification Using Machine Learning Algorithms: An Experimental Analysis,” IEEE Internet Things J., vol. 9, no. 2, pp. 989–1008, 2022, doi: 10.1109/JIOT.2021.3121517.
[16] J. Guan, J. Cai, H. Bai, and I. You, “Deep transfer learning-based network traffic classification for scarce dataset in 5G IoT systems,” Int. J. Mach. Learn. Cybern., vol. 12, no. 11, pp. 3351–3365, 2021, doi: 10.1007/s13042-021-01415-4.
[17] M. M. Raikar, M. S M, M. M. Mulla, N. S. Shetti, and M. Karanandi, “Data Traffic Classification in Software Defined Networks (SDN) using supervised-learning,” Procedia Comput. Sci., vol. 171, pp. 2750–2759, 2020, doi: 10.1016/j.procs.2020.04.299.
[18] G. Aceto, D. Ciuonzo, A. Montieri, and A. Pescapé, “Toward effective mobile encrypted traffic classification through deep learning,” Neurocomputing, vol. 409, pp. 306–315, 2020, doi: 10.1016/j.neucom.2020.05.036.
[19] G. Aceto, D. Ciuonzo, A. Montieri, and A. Pescapé, “DISTILLER: Encrypted traffic classification via multimodal multitask deep learning,” J. Netw. Comput. Appl., vol. 183–184, p. 102985, 2021, doi: 10.1016/j.jnca.2021.102985.
[20] S. Rezaei and X. Liu, “Deep Learning for Encrypted Traffic Classification: An Overview,” IEEE Commun. Mag., vol. 57, no. 5, pp. 76–81, 2019, doi: 10.1109/MCOM.2019.1800819.
 
[21] A. M. Sadeghzadeh, S. Shiravi, and R. Jalili, “Adversarial Network Traffic: Towards Evaluating the Robustness of Deep-Learning-Based Network Traffic Classification,” IEEE Trans. Netw. Serv. Manag., vol. 18, no. 2, pp. 1962–1976, 2021, doi: 10.1109/TNSM.2021.3052888.
[22] J. Höchst, L. Baumgärtner, M. Hollick, and B. Freisleben, “Unsupervised Traffic Flow Classification Using a Neural Autoencoder,” in 2017 IEEE 42nd Conference on Local Computer Networks (LCN), 2017, pp. 523–526. doi: 10.1109/LCN.2017.57.
[23] F. L. Gewers et al., “Principal Component Analysis: A Natural Approach to Data Exploration,” ACM Comput. Surv., vol. 54, no. 4, May 2021, doi: 10.1145/3447755.
[24] D. Deng, “DBSCAN Clustering Algorithm Based on Density,” in 2020 7th International Forum on Electrical Engineering and Automation (IFEEA), 2020, pp. 949–953. doi: 10.1109/IFEEA51475.2020.00199.
[25] M. Raja, P. Hasan, M. Mahmudunnobe,  M. Saifuddin, and S. N. Hasan. Membership determination in open clusters using the DBSCAN Clustering Algorithm. Astronomy and Computing, 47, 100826, 2024. doi: 10.1016/j.ascom.2024.100826.
[26] J. L. Velazquez-Rodriguez, Y. Villuendas-Rey, O. Camacho-Nieto and C. Yanez-Marquez. A novel and simple mathematical transform improves the perfomance of lernmatrix in pattern classification. Mathematics, 8(5), 732, 2020. doi: /10.3390/math8050732.
[27] Y. Zhao and X. Shu. Speech emotion analysis using convolutional neural network (CNN) and gamma classifier-based error correcting output codes (ECOC). Scientific Reports, 13(1), 20398, 2023. doi: 10.1038/s41598-023-47118-4.
Electronic and Cyber Defense
Volume 13, Issue 3 - Serial Number 51
Autumn
Autumn 2025
Pages 1-16

  • Receive Date 08 June 2025
  • Revise Date 04 August 2025
  • Accept Date 07 September 2025
  • Publish Date 23 October 2025