Analysis of Various Network Traffic Classification Techniques for Cyber Security

Authors

  • Namita Parati  Department of CSE, Babasaheb Naik College of Engineering, Pusad, Maharashtra, India
  • Dr Salim Y. Amdani  Department of CSE, Babasaheb Naik College of Engineering, Pusad, Maharashtra, India

Keywords:

Machine Learning, Clustering, Classification, Network, Analysis

Abstract

The quantity of supposed violations in PC networks had not expanded until a couple of years prior. Constant examination has become fundamental to identify any dubious exercises. Network classification is the initial step of organization traffic examination, and it is the center component of organization interruption recognition frameworks (IDS). Albeit the procedures of arrangement have improved and their precision has been upgraded, the developing pattern of encryption and the demand of use engineers to make better approaches to stay away from applications being separated and recognized are among the reasons that this field stays open for additional examination. This paper examines how specialists apply Machine Learning (ML) calculations in a few arrangement procedures, using the factual properties of the organization traffic stream. It additionally frames the following phase of our exploration, which includes examining different characterization procedures (managed, semi-administered, and unaided) that utilization ML calculations to adapt to true organize traffic.

References

  1. Y. Dhote, S. Agrawal, A.J. Deen, A survey on feature selection techniques for internet traffic classification, in: Proceedings of International Conference on Computational Intelligence and Communication Networks, 2016, pp. 1375–1380.
  2. I. Inza, P. Larra, Aga, R. Etxeberria, B. Sierra, Feature subset selection by Bayesian network-based optimization, Artificial Intelligence 123 (1–2) (2000) 157–184.
  3. I. Guyon, J. Weston, S. Barnhill, V. Vapnik, Gene selection for cancer classification using support vector machines, Mach. Learn. 46 (2002) 389–422.
  4. J. Yan, A survey of traffic classification validation and ground truth collection, in: Proceedings of the 8th International Conference on Electronics Information and Emergency Communication, ICEIEC, 2018, pp. 255–259.
  5. I.T. Jolliffe, Principal component analysis, J. Mark. Res. 87 (4) (2002) 513.
  6. Tongaonkar, A.; Keralapura, R.; Nucci, A. Challenges in Network Application Identification. In Proceedings of the 5th USENIX Conference on Large-Scale Exploits and Emergent Threats, San Jose, CA, USA, 25–27 April 2012; p. 1.
  7. Salman, O.; Elhajj, I.; Kayssi, A.; Chehab, A. A Review on Machine Learning Based Approaches for Internet Traffic Classification. Ann. Telecommun. 2020, 673–710. [CrossRef]
  8. Alqudah, N.; Yaseen, Q. Machine Learning for Traffic Analysis: A Review. Procedia Comput. Sci. 2020, 170, 911–916. [CrossRef]
  9. Xie, J.; Yu, F.R.; Huang, T.; Xie, R.; Liu, J.; Wang, C.; Liu, Y. A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges. IEEE Commun. Surv. Tutor. 2019, 21, 393–430. [CrossRef]
  10. Aureli, D.; Cianfrani, A.; Diamanti, A.; Sanchez Vilchez, J.M.; Secci, S. Going Beyond DiffServ in IP Traffic Classification. In Proceedings of the NOMS 2020—2020 IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary, 20–24 April 2020; pp. 1–6.
  11. Zhongsheng, W.; Jianguo, W.; Sen, Y.; Jiaqiong, G. Traffic identification and traffic analysis based on support vector machine. Concurr. Comput. Pract. Exp. 2020, 32, e5292. [CrossRef]
  12. Al-Turjman, F. Smart-city medium access for smart mobility applications in Internet of Things. Trans. Emerg. Telecommun. Technol. 2020, e3723. [CrossRef]
  13. Yao, H.; Gao, P.; Wang, J.; Zhang, P.; Jiang, C.; Han, Z. Capsule Network Assisted IoT Traffic Classification Mechanism for Smart Cities. IEEE Internet Things J. 2019, 6, 7515–7525. [CrossRef]
  14. Miao, Y.; Ruan, Z.; Pan, L.; Zhang, J.; Xiang, Y. Comprehensive analysis of network traffic data. Concurr. Comput. Pract. Exp. 2018,
  15. Perera, P.; Tian, Y.C.; Fidge, C.; Kelly, W. A Comparison of Supervised Machine Learning Algorithms for Classification of Communications Network Traffic. In Neural Information Processing; Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S.M., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 445–454.
  16. Rahman, A.; Jin, J.; Cricenti, A.; Rahman, A.; Yuan, D. A Cloud Robotics Framework of Optimal Task Offloading for Smart City Applications. In Proceedings of the 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA, 4–8 December 2016; pp. 1–7.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2021-10-30

Issue

Volume 8, Issue 5, September-October-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
Namita Parati, Dr Salim Y. Amdani, " Analysis of Various Network Traffic Classification Techniques for Cyber Security" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 5, pp.115-120, September-October-2021.