Efficient Intrusion Detection Using Deep Learning Approaches

Authors

  • V. Sathyendra Kumar Research Scholar, BIHER, Chennai & Assistant Professor, Department of MCA, Annamacharya Institute of Technology & Sciences (Autonomous), New Boyanapalli, Rajampet, Kadapa(Dt.), Phone: 09985666531,
  • A. Muthukumaravel Dean, Faculty of Arts & Science, Professor & HOD- MCA,BIHER, Chennai,

Keywords:

Intrusion Detection, Deep Learning, Accuracy, Network Attacks, Accuracy

Abstract

The main element in life is privacy, even in usual day-to-day life or in the world of the cloud.  The major idea which is beyond the IDS concepts in a system is to discontinue the unknown events occurring from the surrounding or between the systems. It is suggested that the IDS be sent at two focuses. As there is a firewall securing the host organization or the private organization, it is smarter to put the IDS behind the firewall. The IDS sent can work effectively and search for suspicious events inside the organization. The attacks come from outside the host organization, or from the web that is attempting to send information to the host system.  This research work can help in constructing IDS, using deep learning methods such as XGBoost, and MLP that can watch out for the information entering an organization and all the while sort out the unauthorized events.  Among the two methods, MLP produces a better result in terms of accuracy value of about 89.5% compared to XG Boost algorithm which is 88% respectively.

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References

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Purpose of IDS

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Published

19.12.2022

How to Cite

V. Sathyendra Kumar, & A. Muthukumaravel. (2022). Efficient Intrusion Detection Using Deep Learning Approaches. International Journal of Intelligent Systems and Applications in Engineering, 10(2s), 180–183. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2381

Issue

Vol. 10 No. 2s (2022)

Section

Research Article

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