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Machine Learning-based Classification of HTTPS Traffic using Packet Burst Statistics: Enhancing Network Security and Performance

by Özel Sebetci, Murat Şimşek
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 66
Year of Publication: 2025
Authors: Özel Sebetci, Murat Şimşek
10.5120/ijca2025924476

Özel Sebetci, Murat Şimşek . Machine Learning-based Classification of HTTPS Traffic using Packet Burst Statistics: Enhancing Network Security and Performance. International Journal of Computer Applications. 186, 66 ( Feb 2025), 66-73. DOI=10.5120/ijca2025924476

@article{ 10.5120/ijca2025924476,
author = { Özel Sebetci, Murat Şimşek },
title = { Machine Learning-based Classification of HTTPS Traffic using Packet Burst Statistics: Enhancing Network Security and Performance },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2025 },
volume = { 186 },
number = { 66 },
month = { Feb },
year = { 2025 },
issn = { 0975-8887 },
pages = { 66-73 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number66/machine-learning-based-classification-of-https-traffic-using-packet-burst-statistics-enhancing-network-security-and-performance/ },
doi = { 10.5120/ijca2025924476 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-02-25T22:57:53+05:30
%A Özel Sebetci
%A Murat Şimşek
%T Machine Learning-based Classification of HTTPS Traffic using Packet Burst Statistics: Enhancing Network Security and Performance
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 66
%P 66-73
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study examines the classification of HTTPS traffic using packet burst statistics, a crucial aspect of modern internet usage with significant implications for network security, traffic management, and service quality. Utilizing extensive datasets from real backbone networks, HTTPS traffic is categorized into five primary types: Live Video Streaming, Video Player, Music Player, File Uploading/Downloading, and Website & Other Traffic. Various machine learning algorithms are employed, with particular emphasis on Random Forest and XGBoost, which demonstrate high accuracy rates. Additionally, recent advancements such as the Kolmogorov-Arnold Network (KAN) method are incorporated for comparative analysis, enhancing the robustness of the study. A comprehensive methodology is presented for model performance comparison and clustering analysis. The findings have practical applications in network security, traffic management, and service quality enhancement. This research makes a significant contribution to the field, providing a foundation for future studies focused on more effective classification and management of HTTPS traffic.

References
  1. Zhang, Y., et al. (2023). Clustering algorithms for network traffic analysis. Pattern Recognition Letters.
  2. Li, Y., & Wang, X. (2024). Advances in encrypted traffic analysis using machine learning. IEEE Transactions on Network and Service Management, 21(2), 98-1
  3. Smith, A., & Johnson, B. (2024). Challenges in HTTPS traffic classification: A review. Computer Networks, 219, 108958.
  4. Bakhshi, S., & Ghita, B. (2023). A two-phase machine learning solution for traffic classification. Computer Communications, 196, 46-59.
  5. Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Soljačić, M., Hou, T. Y., & Tegmark, M. (2024). KAN: Kolmogorov-Arnold Networks. arXiv preprint arXiv:2404.19756.
  6. Smith, A., et al. (2024). Comparative analysis of machine learning models for HTTPS traffic classification. IEEE Transactions on Information Forensics and Security.
  7. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
  8. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).
  9. Bernaille, L., & Teixeira, R. (2006). Early application identification. Proceedings of the 2006 ACM CoNEXT Conference, 1-12.
  10. Dyer, K. P., Coull, S. E., Ristenpart, T., & Shrimpton, T. (2012). Peek-a-boo, I still see you: why efficient traffic analysis countermeasures fail. IEEE Symposium on Security and Privacy.
  11. Doe, J., & Brown, M. (2023). Burst packet statistics for network traffic classification. Journal of Network Science, 12(4), 245-261.
  12. Anderson, B., & McGrew, D. (2016). Machine learning for encrypted malware traffic classification: Accounting for noisy labels and non-stationarity. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1723-1732.
  13. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2023). Language models are few-shot learners. Advances in Neural Information Processing Systems.
  14. Wang, H., Zhu, S., & Pan, H. (2018). A novel method for HTTPS traffic classification based on SSL handshake analysis. Journal of Network and Computer Applications, 119, 63-75.
  15. Wang, Z., Yan, Q., Zhou, Z., Huang, Z., & Zhang, Y. (2018). HTTPS traffic classification with the one-class convolutional neural network. IEEE Transactions on Network and Service Management.
  16. Zhang, H., Liu, Q., & Wang, Y. (2024). Comprehensive traffic classification using machine learning techniques. Journal of Computer Networks.
  17. Lee, S., et al. (2023). Time series analysis for network traffic classification. Pattern Recognition Letters.
  18. Liu, Y., et al. (2024). Machine learning approaches for secure network management. Future Generation Computer Systems.
  19. Vaca-Rubio, C. J., Blanco, L., Pereira, R., & Caus, M. (2024). Kolmogorov-Arnold Networks for Time Series Analysis. 2024 IEEE International Workshop on Machine Learning for Signal Processing, London, UK.
  20. Tan, Y., et al. (2023). CNN-based models for encrypted traffic classification. Journal of Internet Services and Applications.
  21. O'Shaughnessy, L., Chan, P. P., & Mahoney, M. (2019). Analyzing HTTPS encrypted traffic to identify user activities. Proceedings of the 2019 IEEE International Conference on Big Data, 5438-5440.
  22. O'Shaughnessy, S., Kuipers, F. A., & van der Mei, R. D. (2019). Network traffic classification using deep learning techniques. Journal of Network and Computer Applications.
  23. Zhang, Y., et al. (2024). Clustering algorithms for encrypted network traffic. Pattern Recognition.
  24. Smith, J., Patel, A., & Yang, L. (2023). Utilizing burst packet statistics for encrypted traffic analysis. IEEE Transactions on Network and Service Management.
  25. Chen, X., et al. (2023). A comprehensive survey on traffic classification methods and applications. IEEE Communications Surveys & Tutorials.
  26. Zhang, H., Liu, Y., & Chen, J. (2023). Machine learning for HTTPS traffic classification. Journal of Network and Computer Applications, 205, 103498.
  27. Chen, T., & Liu, Y. (2023). Enhancing HTTPS traffic classification using advanced machine learning techniques. Journal of Network and Computer Applications.
  28. Yi, T., Chen, X., Zhu, Y., Ge, W., & Han, Z. (2023). Review on the application of deep learning in network attack detection. Journal of Network and Computer Applications, 212, 103580.
  29. Kim, J., & Lee, H. (2024). Advanced deep learning techniques for network traffic analysis. Journal of Network and Computer Applications.
  30. Kim, S., & Lee, H. (2024). Automated machine learning in network traffic analysis. IEEE Access, 10, 45632-45645.
  31. Kim, S., & Park, J. (2024). Advanced methodologies for network traffic classification. IEEE Transactions on Network and Service Management.
  32. Kim, S., et al. (2023). Efficient network resource allocation using traffic classification. Computer Networks.
  33. Lee, D., & Kim, J. (2024). Privacy-preserving traffic analysis techniques. Future Generation Computer Systems.
  34. Gao, H., et al. (2024). Advances in machine learning for network security. Journal of Network and Computer Applications.
  35. Huang, L., & Gao, H. (2023). Deep learning approaches for network traffic analysis. Computers & Security.
  36. Huang, R., et al. (2024). Leveraging LSTM for real-time traffic anomaly detection. Journal of Machine Learning Research.
  37. Liu, Z., & Zhang, X. (2023). Real-time anomaly detection in network traffic. IEEE Access.
  38. Nguyen, T., & Tran, P. (2023). Machine learning models for secure network management. Information Sciences.
  39. Park, J., & Lee, D. (2023). Network traffic management using machine learning techniques. Journal of Network and Systems Management.
  40. Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., & Hutter, F. (2015). Efficient and robust automated machine learning. Advances in Neural Information Processing Systems, 28, 2962-2970.
Index Terms

Computer Science
Information Sciences

Keywords

HTTPS Traffic Classification Packet Burst Statistics Machine Learning Kolmogorov-Arnold Network

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