 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: April 2026
Volume 11 | Issue 4
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 119
Article Submitted : 22653
Article Published : 8822
Total Authors : 23281
Total Reviewer : 825
Total Countries : 161
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Comprehensive Review: Network Intrusion Detection System Using Machine Learning on NSL-KDD Dataset |
| Authors Name: | Purusharth Pachauri , Sanju Mahour , Aman Singh , Rohit Sharma |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_209705
|
| Published Paper Id: | IJRTI2602043 |
| Published In: | Volume 11 Issue 2, February-2026 |
| DOI: | 1 |
| Abstract: | This review paper analyzes a comprehensive Network Intrusion Detection System (NIDS) implementation utilizing machine learning and deep learning classifiers on the NSL-KDD dataset. The project demonstrates a multi-faceted approach involving rigorous data preprocessing, feature engineering via Pearson correlation analysis, and comparative evaluation of eight distinct machine learning models. Results show K-Nearest Neighbors achieving peak performance at 98.55% binary classification accuracy, while deep learning models (LSTM, MLP) demonstrated competitive accuracy rates above 96%. This paper examines the project's methodology, implementation, performance metrics, and implications for cybersecurity applications. The research contributes to the growing body of evidence supporting machine learning's efficacy in automated intrusion detection while identifying challenges and future research directions. |
| Keywords: | Intrusion Detection System, NSL-KDD Dataset, Machine Learning, Deep Learning, LSTM, Autoencoder, Network Security, Cybersecurity |
| Cite Article: | "Comprehensive Review: Network Intrusion Detection System Using Machine Learning on NSL-KDD Dataset", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 2, page no.a331-a339, February-2026, Available :http://www.ijrti.org/papers/IJRTI2602043.pdf |
| Downloads: | 000232 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2602043
Registration ID:209705
Published In: Volume 11 Issue 2, February-2026
DOI (Digital Object Identifier): 1
Page No: a331-a339 Country: Bichpuri, Agra, Uttar Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2602043 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2602043 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||