ISSN2320-2882
Impact Factor9.17
UGC ParametersAligned
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CrossRefDOI
ESTD2013
Peer ReviewTransparent
AccessOpen
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Paper Title
A Hybrid Machine Learning and Deep Learning Approach for Enhanced Intrusion Detection
Authors
Eak Nath Dubey , Rakesh Kumar Lodhi , Rakesh Kumar Tiwari
Keywords
Intrusion Detection System (IDS), Machine Learning (ML), Deep Learning (DL), Hybrid Model, cyber security, Anomaly Detection, Zero-Day Attacks
Abstract
In the rapidly evolving digital environment of the present times, traditional intrusion detection systems (IDS) are increasingly struggling to detect highly advanced cyber-attacks. Traditional rule-based methods rarely detect unknown or zero-day attacks and are non-scalable or suffer from a high rate of false positives. To overcome these limitations, recent advances in artificial intelligence have introduced promising techniques i.e., machine learning (ML) and deep learning (DL) which enhance the precision and responsiveness of IDS. This work proposes a hybrid IDS model that combines the interpretability and efficiency of ML with the deep feature learning as well as pattern recognition capabilities of DL. The proposed hybrid model employs both signature-based as well as anomaly-based detection methods in an effort to maximize the detection rate of threats while minimizing false alarms. The proposed model is evaluated with standard benchmark datasets such as NSL-KDD as well as CICIDS2017 in an effort to demonstrate its functionality in real-world applications. Results demonstrate significant detection accuracy, scalability, and responsiveness enhancements compared to traditional and stand-alone AI-based approaches. The research provides a valuable and intelligent solution to modern cyber security issues and opens the door to future developments in explainable, adaptive, and real-time IDS systems.
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IJCRT — journal style
"A Hybrid Machine Learning and Deep Learning Approach for Enhanced Intrusion Detection", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 2, page no.b967-b977, February 2026, Available :https://ijcrt.org/papers/IJCRT2602218.pdf
APA — 7th edition
Dubey, E. N., Lodhi, R. K., & Tiwari, R. K. (2026). A Hybrid Machine Learning and Deep Learning Approach for Enhanced Intrusion Detection. International Journal of Creative Research Thoughts, 14(2), b967-b977. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602218
MLA — 9th edition
Dubey, Eak Nath, et al. "A Hybrid Machine Learning and Deep Learning Approach for Enhanced Intrusion Detection." International Journal of Creative Research Thoughts, vol. 14, no. 2, 2026, pp. b967-b977, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602218.
Chicago — 17th, bibliography
Dubey, Eak Nath, Rakesh Kumar Lodhi, and Rakesh Kumar Tiwari. "A Hybrid Machine Learning and Deep Learning Approach for Enhanced Intrusion Detection." International Journal of Creative Research Thoughts 14, no. 2 (2026): b967-b977. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602218.
Harvard — author–date
Dubey, E.N., Lodhi, R.K. and Tiwari, R.K. (2026) 'A Hybrid Machine Learning and Deep Learning Approach for Enhanced Intrusion Detection', International Journal of Creative Research Thoughts, 14(2), pp. b967-b977. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602218
IEEE — numbered reference
E. N. Dubey, R. K. Lodhi and R. K. Tiwari, "A Hybrid Machine Learning and Deep Learning Approach for Enhanced Intrusion Detection," IJCRT, vol. 14, no. 2, pp. b967-b977, Feb. 2026.
Vancouver — biomedical
Dubey EN, Lodhi RK, Tiwari RK. A Hybrid Machine Learning and Deep Learning Approach for Enhanced Intrusion Detection. IJCRT. 2026 Feb;14(2):b967-b977.
AMA — 11th edition
Dubey EN, Lodhi RK, Tiwari RK. A Hybrid Machine Learning and Deep Learning Approach for Enhanced Intrusion Detection. IJCRT. 2026;14(2):b967-b977. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602218
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2602218,
author = {Eak Nath Dubey and Rakesh Kumar Lodhi and Rakesh Kumar Tiwari},
title = {A Hybrid Machine Learning and Deep Learning Approach for Enhanced Intrusion Detection},
journal = {International Journal of Creative Research Thoughts},
volume = {14},
number = {2},
pages = {b967-b977},
year = {2026},
month = {February},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602218}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Dubey, Eak Nath
AU - Lodhi, Rakesh Kumar
AU - Tiwari, Rakesh Kumar
TI - A Hybrid Machine Learning and Deep Learning Approach for Enhanced Intrusion Detection
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 14
IS - 2
PY - 2026
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602218
SP - b967-b977
ER -
Issue
Volume 14 Issue 2,
February-2026
Pages : b967-b977
Article Information
- Published In
- International Journal of Creative Research Thoughts — Volume 14, Issue 2, February 2026
- Pages
- b967-b977
- Published
- February 2026
- Registration ID
- IJCRT_301341
- Published Paper ID
- IJCRT2602218
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- Buxar, Bihar, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 183
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602218
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2602218.pdf
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International Journal of Creative Research Thoughts
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