Visualisasi Trafik Jaringan Dengan Metode Support Vector Machine (SVM) (Studi Kasus: Universitas Indo Global Mandiri)
DOI:
https://doi.org/10.36982/jiig.v12i2.1939Abstract
Limited network resources and the increasing number of internet users in the current digital era have an impact on high traffic which results in decreased access speed to internet services. This is also a problem that occurs at the Indo Global Mandiri University (UIGM) Palembang, causing access to academic services to be slow. The purpose of this research is to identify the types of network traffic patterns which are then carried out by the process of grouping and visualizing these types of traffic. The data in this study were taken in real-time at the UIGM campus. The data obtained is the result of responses which are then extracted. The extraction results are processed using the Support Vector Machine (SVM) method for the process of grouping and visualizing data. The results of this study can distinguish types of traffic based on communication protocols, namely tcp and udp, where the results of the experiment were carried out six times with the results being the first experiment where 99.7% TCP and 0.1% for UDP, the second experiment 97.6% for TCP and 1.1% for UDP , trial three 99.7 % TCP and 0.2% UDP, trial four 97.5% and 1.3% UDP, trial five 99.5 TCP and 02% UDP, and the sixth or final try 97.7% TCP and 1.1% UDP. The data from the use of the SVM method obtained several types of traffic such as games by 0.4%, mail 0.2%, multimedia 0.4% and the web by 82.8% and this research still produces data that the pattern is not yet recognized by 15.5%
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Keywords : Network Traffic, Classification, Support Vector Mesin
References
Alshammari, R., & Zincir-Heywood, A. N. (2010). An investigation on the identification of voip traffic: Case study on Gtalk and Skype. Proceedings of the 2010 International Conference on Network and Service Management, CNSM 2010, 310–313. https://doi.org/10.1109/CNSM.2010.5691210
Awad, H., Ibrahim, H., Radhi, O., Al, A., Al-namari, M. A., & Mohamedali, G. (2016). Internet Traffic Classification using Machine Learning Approach : Datasets Validation Issues. Proceedings of 2016 Conference of Basic Sciences and Engineering Studies, SGCAC 2016, 158–166. https://doi.org/10.1109/SGCAC.2016.7458022
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., … Caicedo, O. M. (2018). A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. Journal of Internet Services and Applications, Vol. 9, pp. 1–99. https://doi.org/10.1186/S13174-018-0087-2
Bujlow, T., Carela-Español, V., & Barlet-Ros, P. (2015). Independent comparison of popular DPI tools for traffic classification. Computer Networks. https://doi.org/10.1016/j.comnet.2014.11.001
Byun, H., & Lee, S. W. (2003). A survey on pattern recognition applications of support vector machines. International Journal of Pattern Recognition and Artificial Intelligence, 17(3), 459–486. https://doi.org/10.1142/S0218001403002460
Fan, Z., & Liu, R. (2017). Investigation of machine learning based network traffic classification. Proceedings of the International Symposium on Wireless Communication Systems. https://doi.org/10.1109/ISWCS.2017.8108090
Gu, R., Wang, H., & Ji, Y. (2010). Early Traffic Identification Using Bayesian Networks. Communication, 564–568.
Hao, S., Hu, J., Liu, S., Song, T., Guo, J., & Liu, S. (2015). Improved SVM method for internet traffic classification based on feature weight learning. ICCAIS 2015 - 4th International Conference on Control, Automation and Information Sciences, 102–106. https://doi.org/10.1109/ICCAIS.2015.7338641
Jesudasan, R. N., Branch, P., & But, J. (2010). Generic Attributes for Skype Identification Using Machine Learning. (August), 1–7.
Kong, L., Huang, G., Wu, K., Tang, Q., & Ye, S. (2018). Comparison of Internet Traffic Identification on Machine Learning Methods. 2018 International Conference on Big Data and Artificial Intelligence (BDAI), 38–41. https://doi.org/10.1109/BDAI.2018.8546682
Liu, C. C., Chang, Y., Tseng, C. W., Yang, Y. T., Lai, M. S., & Chou, L. Der. (2018). SVM-based Classification Mechanism and Its Application in SDN Networks. 2018 10th International Conference on Communication Software and Networks, ICCSN 2018, 45–49. https://doi.org/10.1109/ICCSN.2018.8488219
Networks, C., Dhote, Y., Agrawal, S., & Deen, A. J. (2015). A Survey on Feature Selection Techniques for Internet Traffic Classification. Proceedings - 2015 International Conference on Computational Intelligence and Communication Networks, CICN 2015, 1375–1380. https://doi.org/10.1109/CICN.2015.267
Nguyen, T. T. T., & Armitage, G. (2008). A Survey of Techniques for Internet Traffic Classification Using Machine Learning. Communications Surveys & Tutorials, IEEE, 10(4), 56–76. https://doi.org/10.1109/SURV.2008.080406
Oklilas, A. F., & Tasmi. (2017). Monitoring and Indentification Packet in Wireless With Deep Packet Inspection Method. International Conference on Recent Trends in Physics 2016 (ICRTP2016) IAES International Conference on Electrical Engineering, Computer Science and Informatics, 365, 011001. https://doi.org/10.1088/1742-6596/365/1/011001
Parziale, L., Britt, D. T., Davis, C., Forrester, J., Liu, W., Matthews, C., & Rosselot, N. (2006). TCP/IP Tutorial and Technical Overview. In TCP/IP Tutorial and Technical Overview (8th ed., Vol. 8). IBM Corporation.
Robertazzi, G. T. (2017). Introduction To Computer Networking. https://doi.org/10.1007/978-3-319-53103-8
Shafiq, M., Yu, X., Laghari, A. A., Yao, L., Karn, N. K., & Abdessamia, F. (2017). Network Traffic Classification techniques and comparative analysis using Machine Learning algorithms. 2016 2nd IEEE International Conference on Computer and Communications, ICCC 2016 - Proceedings, 2451–2455. https://doi.org/10.1109/CompComm.2016.7925139
Siqueira, I. G., Ruiz, L. B., & Loureiro, a. a. F. (2007). Coverage area management for wireless sensor networks. Internation Journal of Network Management, (October 2005), 17–31. https://doi.org/10.1002/nem
Soysal, M., & Schmidt, E. G. (2010). Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison. Performance Evaluation, 67(6), 451–467. https://doi.org/10.1016/j.peva.2010.01.001
Wang, C., Zhang, H., Ye, Z., & Du, Y. (2015). A peer to peer traffic identification method based on support vector machine and artificial bee colony algorithm. Proceedings of the 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2015, 2(September), 982–986. https://doi.org/10.1109/IDAACS.2015.7341451
Wu, Z., Dong, Y., Yang, L., & Tang, P. (2018). A New Structure for Internet Video Traffic Classification Using Machine Learning. Proceedings - 2018 6th International Conference on Advanced Cloud and Big Data, CBD 2018, (61271233), 322–327. https://doi.org/10.1109/CBD.2018.00064
Yang, J., Narantuya, J., & Lim, H. (2019). Bayesian Neural Network Based Encrypted Traffic Classification using Initial Handshake Packets. Proceedings - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume, DSN-S 2019, 19–20. https://doi.org/10.1109/DSN-S.2019.00015
Zamfir, S., Balan, T., Sandu, F., & Costache, C. (2016). Solutions for deep packet inspection in industrial communications. IEEE International Conference on Communications, 2016-Augus, 153–158. https://doi.org/10.1109/ICComm.2016.7528337
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