International Journal of Research and Development in Engineering Sciences (IJRDES ) | Intrusion Detection System Using Machine Learning Techniques

Intrusion Detection System Using Machine Learning Techniques

Network intrusion detection technology plays a vital role in guaranteeing network security. The primary goal is to continually monitor the current state of the network, identify abnormal behaviour in the network state, and notify network administrators in time. The speed and accuracy of an intrusion detection system (IDS) are important to the availability and dependability of today's network. In response to the difficulties of high false alarm rates, poor detection efficiency, and restricted functionality prevalent in IDS, this study first studies the application of machine learning approaches to network intrusion detection. Since machine learning algorithms can automatically extract characteristics from intrusion data and prevent human feature extraction, an intrusion detection approach based on a decision tree classifier is presented. The approach has been enhanced by integrating the Inception module for optimum separation of intrusion functions. The initialization module employs a classification structure with distinct filters, utilizing varying size classification kernels in each row to operate in several layers, and the diverse features of network incursions in the dataset are identified and categorized by stacking.

Research Type: Applied Research
Paper Type: Experimental Research Paper
Vol.6 , Issue 2 , Pages: 4 - 7, Apr 2024
Published on: 25 Apr, 2024
Issue Type: Regular

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

About Authors:

N SATISH KUMAR

India

Mahatma Gandhi Institute of Technology

Copyright © 2024, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Corresponding Author: N SATISH KUMAR, satish.nallamilli54@gmail.com

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Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.