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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
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Issue: March 2026
Volume 11 | Issue 3
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21579
Article Published : 8531
Total Authors : 22438
Total Reviewer : 805
Total Countries : 159
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| Paper Title: | Network Intrusion Detection Systems: A Comprehensive and Human-Centric Review of Machine Learning and Deep Learning Approaches |
| Authors Name: | Dr. Rolly Gupta , Mohd Hamza , Lakshya Bhatt , Avikal Bhatt |
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IJRTI_207181
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| Published Paper Id: | IJRTI2511049 |
| Published In: | Volume 10 Issue 11, November-2025 |
| DOI: | |
| Abstract: | Abstract—As the world’s dependence on digital communication deepens, the challenge of defending networks against sophisticated intrusions has become one of the defining technical problems of our era. Network Intrusion Detection Systems (NIDS) act as sentinels that analyse traffic to identify threats and anomalies. Yet the classical rule-based or signature-driven methods that once formed the backbone of intrusion detection are increasingly inadequate. Machine Learning (ML) and Deep Learning (DL) offer a paradigm shift, enabling systems to learn complex relationships directly from traffic data and to adapt as attack behaviours evolve. This review provides a human-centred synthesis of how ML and DL techniques have been applied to NIDS over roughly the last decade. It describes core algorithms, datasets, and evaluation practices, while also exploring practical questions that matter to real-world deployment—interpretability, resource constraints, and integration into security operations. By combining technical depth with an accessible narrative, we aim to make current research understandable to both practitioners and new researchers, outlining not only what works in the lab but what endures in production. |
| Keywords: | network intrusion firewall |
| Cite Article: | "Network Intrusion Detection Systems: A Comprehensive and Human-Centric Review of Machine Learning and Deep Learning Approaches", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.a395-a400, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511049.pdf |
| Downloads: | 000209 |
| 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 |
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Published Paper ID: IJRTI2511049
Registration ID:207181
Published In: Volume 10 Issue 11, November-2025
DOI (Digital Object Identifier):
Page No: a395-a400 Country: Ghaziabad, Uttar Pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2511049 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2511049 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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