ISSN2456-3315
Impact Factor10.57
UGC CAREApproved
Low Cost₹599 / $45
CrossRefDOI
ESTD2016
Peer ReviewTransparent
AccessOpen
Submission: Always Open
Paper Title
Network Intrusion detection system using machine learning
Authors
Fayas Muhammed , Johann Dominic Thomas , Akash Biju , Andrew Sebi Varghese , Adithyan A S
Keywords
Network Intrusion Detection Systems (NIDS); Machine Learning; Deep Learning; Cybersecurity; Feature Selection; Anomaly Detection; Network Security; Classification; Supervised Learning; Unsupervised Learning
Abstract
Network Intrusion Detection Systems
(NIDS) are vital in cybersecurity due to the increasing
complexity of cyber attacks. This survey reviews machine
learning approaches in NIDS, examining their development,
current status, and future directions. We categorise and
evaluate traditional algorithms, deep learning methods, and
hybrid approaches, discussing key datasets, feature selec
tion, and performance metrics. The study addresses chal
lenges such as class imbalance, high false positive rates,
and real-time detection, while also exploring trends like
federated learning, transfer learning, and explainable AI
within NIDS. Despite promising results, ML-based NIDS
face challenges in achieving optimal performance in dy
namic network environments, presenting areas for further
research and enhancement.
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How To Cite
Choose the style your journal or department asks for, then copy it. Every version below is generated from this paper's own record.
IJRTI — journal style
"Network Intrusion detection system using machine learning", IJRTI - International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.10, Issue 1, page no.a214-a218, January-2025, Available :https://ijrti.org/papers/IJRTI2501030.pdf
APA — 7th edition
Muhammed, F., Thomas, J. D., Biju, A., Varghese, A. S., & S, A. A. (2025). Network Intrusion detection system using machine learning. International Journal for Research Trends and Innovation, 10(1), a214-a218. https://ijrti.org/viewpaperforall.php?paper=IJRTI2501030
MLA — 9th edition
Muhammed, Fayas, et al. "Network Intrusion detection system using machine learning." International Journal for Research Trends and Innovation, vol. 10, no. 1, 2025, pp. a214-a218, https://ijrti.org/viewpaperforall.php?paper=IJRTI2501030.
Chicago — 17th, bibliography
Muhammed, Fayas, et al. "Network Intrusion detection system using machine learning." International Journal for Research Trends and Innovation 10, no. 1 (2025): a214-a218. https://ijrti.org/viewpaperforall.php?paper=IJRTI2501030.
Harvard — author–date
Muhammed, F. et al. (2025) 'Network Intrusion detection system using machine learning', International Journal for Research Trends and Innovation, 10(1), pp. a214-a218. Available at: https://ijrti.org/viewpaperforall.php?paper=IJRTI2501030
IEEE — numbered reference
F. Muhammed, J. D. Thomas, A. Biju, A. S. Varghese and A. A. S, "Network Intrusion detection system using machine learning," IJRTI, vol. 10, no. 1, pp. a214-a218, Jan. 2025.
Vancouver — biomedical
Muhammed F, Thomas JD, Biju A, Varghese AS, S AA. Network Intrusion detection system using machine learning. IJRTI. 2025 Jan;10(1):a214-a218.
AMA — 11th edition
Muhammed F, Thomas JD, Biju A, Varghese AS, S AA. Network Intrusion detection system using machine learning. IJRTI. 2025;10(1):a214-a218. Accessed at https://ijrti.org/viewpaperforall.php?paper=IJRTI2501030
BibTeX — Zotero, Mendeley, LaTeX
@article{IJRTI2501030,
author = {Fayas Muhammed and Johann Dominic Thomas and Akash Biju and Andrew Sebi Varghese and Adithyan A S},
title = {Network Intrusion detection system using machine learning},
journal = {International Journal for Research Trends and Innovation},
volume = {10},
number = {1},
pages = {a214-a218},
year = {2025},
month = {January},
issn = {2456-3315},
url = {https://ijrti.org/viewpaperforall.php?paper=IJRTI2501030}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Muhammed, Fayas
AU - Thomas, Johann Dominic
AU - Biju, Akash
AU - Varghese, Andrew Sebi
AU - S, Adithyan A
TI - Network Intrusion detection system using machine learning
T2 - International Journal for Research Trends and Innovation
JA - IJRTI
VL - 10
IS - 1
PY - 2025
SN - 2456-3315
UR - https://ijrti.org/viewpaperforall.php?paper=IJRTI2501030
SP - a214-a218
ER -
Issue
Volume 10 Issue 1,
January-2025
Pages : a214-a218
Other Publication Details
Paper Reg. ID
IJRTI_200235
Published Paper ID
IJRTI2501030
Downloads
205,625
Research Area
Computer Science & TechnologyÂ
Country
Kottayam, Kerala, India
Published
January 2025
Published Paper PDF
https://ijrti.org/papers/IJRTI2501030.pdf
Published Paper URL
https://ijrti.org/viewpaperforall.php?paper=IJRTI2501030
About Publisher
International Journal for Research Trends and Innovation
Published by IJRTI (JW Publication)
2456-3315
ISSN
10.57
Impact Factor
2016
ESTD Year
Open
Access
Impact Factor 10.57 calculated by Google Scholar and Semantic Scholar.
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The content, data and findings in this article are based on the authors’ research and have been
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Declarations
Funding
No external funding was received for this study.
Conflict of Interest
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank the reviewers and the editorial board of International Journal for Research Trends and Innovation for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.
