##plugins.themes.academic_pro.article.sidebar##

Download
pdf
Statistic
Read Counter : 201 Download : 139

##plugins.themes.academic_pro.article.main##

Abstract

IP traffic classification is significant for Internet service providers and other private and public organizations, for example in various tasks such as bandwidth scheduling, network error detection, Internet service quality analysis, pricing for users who use specific Internet applications, tracking Internet traffic data and security for specific government agencies. Online sequential extreme learning machine is a method of online learning solving the problem of observing data of different sizes. However, the input weights and OSELM hidden layer biases are randomly determined. This results in an incorrect classification result. In the proposed method, the data are classified using anonline sequential extreme learning machine algorithm. Certainsoftware based on artificial bee colony algorithm (ABC -OSELM) was developed to select the parameters of the sequential fast online machine learning algorithm. The simulation results show that the proposed method has achieved a 7% improvement in accuracy criteria compared to the base paper.

Keywords

IP Traffic Classification Artificial Bee Colony Algorithm Fast Machine Learning Online Sequential Extreme Learning Machine

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Dhulfiqar Mahmood Tawfeeq Al-Saada, & Maryam Thabet Hussein Al-Khazraji. (2022). A new approach for internet traffic classification: Artificial Bee Colony algorithm-OSELM. Texas Journal of Engineering and Technology, 8, 23–29. Retrieved from https://zienjournals.com/index.php/tjet/article/view/1576

References

  1. Tahaei, H., Afifi, F., Asemi, A., Zaki, F., & Anuar, N. B. (2020). The rise of traffic classification in IoT networks: A survey. Journal of Network and Computer Applications, 154, 102538.
  2. Abualigah, L., Diabat, A., & Geem, Z. W. (2020). A comprehensive survey of the harmony search algorithm in clustering applications. Applied Sciences, 10(11), 3827.
  3. Singh, K., & Agrawal, S. (2011, April). Comparative analysis of five machine learning algorithms for IP traffic classification. In 2011 International conference on emerging trends in networks and computer communications (ETNCC) (pp. 33-38). IEEE.
  4. Chen, Y., Xie, X., Zhang, T., Bai, J., & Hou, M. (2020). A deep residual compensation extreme learning machine and applications. Journal of Forecasting, 39(6), 986-999.
  5. Li, Y., Qiu, R., & Jing, S. (2018). Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid. PloS one, 13(2), e0192216.
  6. Ertam, F., & Avcı, E. (2017). A new approach for internet traffic classification: GA-WK-ELM. Measurement, 95, 135-142.
  7. Ertam, F., & Avcı, E. (2016). Network traffic classification via kernel based extreme learning machine. International Journal of Intelligent Systems and Applications in Engineering, 109-113.
  8. Gautam, C., Tiwari, A., & Leng, Q. (2017). On the construction of extreme learning machine for online and offline one-class classification—An expanded toolbox. Neurocomputing, 261, 126-143.
  9. Bakhshi, T., & Ghita, B. (2016). On internet traffic classification: A two-phased machine learning approach. Journal of Computer Networks and Communications, 2016.
  10. Ghafari, J., Herbert, E., Senecal, S., Migault, D., Francfort, S., & Liu, T. (2014). Extreme learning machines for Internet traffic classification. In ESANN.
  11. Zeng, Y., Gu, H., Wei, W., & Guo, Y. (2019). $ Deep-full-range $: A deep learning based network encrypted traffic classification and intrusion detection framework. IEEE Access, 7, 45182-45190.
  12. Zhou, H., Wang, Y., Lei, X., & Liu, Y. (2017, December). A method of improved CNN traffic classification. In 2017 13th International Conference on Computational Intelligence and Security (CIS) (pp. 177-181). IEEE.
  13. Lim, H. K., Kim, J. B., Heo, J. S., Kim, K., Hong, Y. G., & Han, Y. H. (2019, February). Packet-based network traffic classification using deep learning. In 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (pp. 046-051). IEEE.
  14. Hasibi, R., Shokri, M., & Dehghan, M. (2019). Augmentation scheme for dealing with imbalanced network traffic classification using deep learning. arXiv preprint arXiv:1901.00204.
  15. Wang, P., Ye, F., Chen, X., & Qian, Y. (2018). Datanet: Deep learning based encrypted network traffic classification in sdn home gateway. IEEE Access, 6, 55380-55391.
  16. Wang, W., Zhu, M., Wang, J., Zeng, X., & Yang, Z. (2017, July). End-to-end encrypted traffic classification with one-dimensional convolution neural networks. In 2017 IEEE International Conference on Intelligence and Security Informatics (ISI) (pp. 43-48). IEEE.
  17. Aceto, G., Ciuonzo, D., Montieri, A., & Pescapé, A. (2018, June). Mobile encrypted traffic classification using deep learning. In 2018 Network traffic measurement and analysis conference (TMA) (pp. 1-8). IEEE.
  18. Zou, Z., Ge, J., Zheng, H., Wu, Y., Han, C., & Yao, Z. (2018, June). Encrypted traffic classification with a convolutional long short-term memory neural network. In 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) (pp. 329-334). IEEE.
  19. Lotfollahi, M., Siavoshani, M. J., Zade, R. S. H., & Saberian, M. (2020). Deep packet: A novel approach for encrypted traffic classification using deep learning. Soft Computing, 24(3), 1999-2012.
  20. Chen, Z., He, K., Li, J., & Geng, Y. (2017, December). Seq2img: A sequence-to-image based approach towards ip traffic classification using convolutional neural networks. In 2017 IEEE International conference on big data (big data) (pp. 1271-1276). IEEE.
  21. Chen, C. N., Hoang, T. T., & Cho, M. Y. (2019). Parameter optimisation of support vector machine using mutant particle swarm optimisation for diagnosis of metal-oxide surge arrester conditions. Journal of Experimental & Theoretical Artificial Intelligence, 31(1), 163-175.
  22. Blondin, J., & Saad, A. (2010, August). Metaheuristic techniques for support vector machine model selection. In 2010 10th International Conference on Hybrid Intelligent Systems (pp. 197-200). IEEE.
  23. Draper-Gil, G., Lashkari, A. H., Mamun, M. S. I., & Ghorbani, A. A. (2016, February). Characterization of encrypted and vpn traffic using time-related. In Proceedings of the 2nd international conference on information systems security and privacy (ICISSP) (pp. 407-414).
  24. Wang, W., Zhu, M., Wang, J., Zeng, X., & Yang, Z. (2017, July). End-to-end encrypted traffic classification with one-dimensional convolution neural networks. In 2017 IEEE International Conference on Intelligence and Security Informatics (ISI) (pp. 43-48). IEEE.