Network Intrusion Detection Using Deep Learning

Authors

  • Blessy S  B.E Scholar, Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
  • Samyuktha Ravi  B.E Scholar, Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
  • Shurabthini S  B.E Scholar, Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
  • Amudha P  Professor,Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India

Keywords:

Intrusion Detection, Deep Learning, Convolution Neural Network

Abstract

Intrusion-detection system aims at detecting attacks against computer systems and networks or, in general, against information systems. Though various part of encryption techniques and firewalls are used to block common attacks, attackers always finds a way in intruding the network. This creates a huge need of dynamic network intrusion detection system where it can protect from nearly all types of attacks. Since dynamic nature of the system is concerned, it should be able to provide with a self-learning mechanism. Deep learning is one of the mostly preferred algorithms in dynamic learning. This paper proposes Convolution Neural Network (CNN) algorithm for intrusion detection.

References

  1. A. Krishna, A. Lal M.A., A. J. Mathewkutty, D. S. Jacob and M. Hari, Intrusion Detection and Prevention System Using Deep Learning, In Proceedings of International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 273-278, 2020.
  2. Y. Dong, R. Wang and J. He, Real-Time Network Intrusion Detection System Based on Deep Learning, In Proceedings of IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), pp. 1-4, 2019.
  3. R. Devakunchari, Sourabh, Prakhar Malik, A Study of Cyber Security using Machine Learning Techniques, International Journal of Innovative Technology and Exploring Engineering (IJITEE), pp.2278- 3075,Vol.8, Issue-7, 2019.
  4. W. Peng, X. Kong, G. Peng, X. Li and Z. Wang, Network Intrusion Detection Based on Deep Learning, In Proceedings of International Conference on Communications, Information System and Computer Engineering (CISCE), pp. 431-435, 2019.
  5. M. Ishaque and L. Hudec, Feature extraction using Deep Learning for Intrusion Detection System, In Proceedings of 2nd International Conference on Computer Applications & Information Security (ICCAIS), pp. 1-5, 2019.
  6. C. Yin, Y. Zhu, J. Fei and X. He, A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks, IEEE Access, vol. 5, pp. 21954-21961, 2017.
  7. G. Zhao, C. Zhang and L. Zheng, Intrusion Detection Using Deep Belief Network and Probabilistic Neural Network, IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), pp. 639-642, 2017.
  8. https://www.kaggle.com/venkatakanumuru/kddcup99csv

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2021-06-30

Issue

Volume 8, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

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

How to Cite

[1]
Blessy S, Samyuktha Ravi, Shurabthini S, Amudha P "Network Intrusion Detection Using Deep Learning" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 3, pp.469-472, May-June-2021.