 |
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: | Network Intrusion Detection and Forensic Logging System Using Machine Learning |
| Authors Name: | Yuvaraj , Raja Sekar M , Yogesh I , R. Alex Giftson |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_211176
|
| Published Paper Id: | IJRTI2604070 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | In recent years, the rapid expansion of digital technologies, cloud computing, and internet-based services has significantly increased the vulnerability of network systems to a wide range of cyber threats. Modern organizations rely heavily on interconnected systems for data storage, communication, and business operations, making them attractive targets for cyber attackers. As a result, the frequency and sophistication of cyberattacks such as Distributed Denial of Service (DDoS), phishing, ransomware, and advanced persistent threats (APTs) have increased dramatically. Traditional Intrusion Detection Systems (IDS), which primarily rely on predefined signatures or static rule-based mechanisms, are no longer sufficient to handle these evolving threats. While signature-based systems are effective in detecting known attack patterns, they fail to identify unknown or zero-day attacks. Similarly, anomaly-based systems often suffer from high false positive rates, leading to unnecessary alerts and reduced system reliability. These limitations highlight the need for more intelligent and adaptive security solutions. To address these challenges, this research proposes a Machine Learning-based Network Intrusion Detection and Forensic Logging System (NIDS) that enhances the detection and classification of cyber threats in real time. The proposed system leverages multiple machine learning algorithms, including Random Forest, Decision Tree, K-Nearest Neighbors (KNN), Naive Bayes, and Logistic Regression, to analyze network traffic and accurately classify it as normal or malicious. By utilizing supervised learning techniques, the system is capable of learning complex patterns and detecting previously unseen attacks. The system is trained and evaluated using the NSL-KDD dataset, which is widely recognized as a benchmark dataset in intrusion detection research. The dataset includes various types of network traffic along with labeled attack categories such as DoS, Probe, Remote-to-Local (R2L), and User-to-Root (U2R). To ensure effective model performance, data preprocessing techniques such as feature encoding, normalization, and noise removal are applied. The performance of the models is evaluated using standard metrics including accuracy, precision, recall. |
| Keywords: | |
| Cite Article: | "Network Intrusion Detection and Forensic Logging System Using Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a519-a525, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604070.pdf |
| Downloads: | 00086 |
| 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: IJRTI2604070
Registration ID:211176
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a519-a525 Country: Karur, Tamilnadu , India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604070 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604070 |
| 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 | |||