 |
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 : 23355
Article Published : 9033
Total Authors : 23952
Total Reviewer : 831
Total Countries : 162
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Intrusion Detection System Using Machine Learning For Real-Time Network |
| Authors Name: | Muruganandham.L , Malararasan.M , Malararasi.M , Madhan.P , Hemalatha.V |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_211326
|
| Published Paper Id: | IJRTI2604113 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | With the rapid growth of network-based systems, ensuring security against cyber threats has become a critical challenge. Traditional intrusion detection systems often fail to detect new or evolving attack patterns in real time. To address this issue, this project presents a Machine Learning-based Intrusion Detection System (IDS) that leverages the Random Forest algorithm for the accurate and efficient detection of network attacks. The system is developed using the NSL-KDD dataset, from which essential features such as duration, protocol type, service, flag, source bytes, destination bytes, and traffic-based metrics are extracted. Categorical features are transformed into numerical values to make them suitable for machine learning processing. The trained Random Forest classifier is capable of identifying multiple types of network traffic, including Normal, Denial of Service (DoS), Probe, and Unauthorized Access (R2L) attacks. For real-time intrusion monitoring, the system integrates Scapy to capture live network packets, extracts relevant features, and passes them to the trained model to predict traffic types. If an intrusion is detected, an alert message is instantly generated, displaying critical details such as attack type, source IP, destination IP, and timestamp. Furthermore, a user-friendly web interface provides functionalities to view training datasets, analyze attack records, perform manual detection, and monitor traffic via an analytics dashboard. This approach provides scalability, adaptability, and significant improvements in detection accuracy for modern network environments. |
| Keywords: | |
| Cite Article: | "Intrusion Detection System Using Machine Learning For Real-Time Network", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a803-a808, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604113.pdf |
| Downloads: | 00068 |
| 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: IJRTI2604113
Registration ID:211326
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a803-a808 Country: Karur, Tamilnadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604113 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604113 |
| 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 | |||