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Paper Title
Dual-Stage Data Fusion and Hybrid Deep Learning Framework for Robust Intrusion Detection in Network Traffic
Authors
Ram Naresh Sharma , Jitendra Singh Kushwah , Pritaj Yadav
Keywords
Intrusion Detection System, Hybrid Deep Learning, Data Fusion, Network Security, Cyberattack Detection, Temporal and Spatial Features, Feature Engineering
Abstract
This study presents a cutting-edge intrusion detection framework that leverages a dual-stage data fusion strategy combined with hybrid deep learning models to improve the detection and classification of cyberattacks in network traffic. The proposed approach integrates behavioral ratio-based features, traffic intensity metrics, temporal activity patterns, and protocol-level interactions to construct a comprehensive fused feature space. A neural encoder network further refines these features, enabling the models to capture both spatial correlations and temporal dependencies, thereby enhancing discrimination between benign and malicious traffic. Two hybrid architectures FusionNet-BiLSTM IDS (CNN-BiLSTM) and FusionNet-GRU IDS (CNN-GRU) were implemented and evaluated on the CIC-IDS 2018 dataset. Severe class imbalance, a common challenge in intrusion detection, was effectively addressed using SMOTE-ENN, which synthetically oversamples minority classes and removes noisy majority-class samples, improving generalization and detection of low-frequency attacks. Exploratory data analysis guided feature engineering and model design. Performance evaluation demonstrated that FusionNet-BiLSTM achieved high accuracy (0.9871), precision (0.9872), recall (0.9871), F1-score (0.9871), and low loss (0.04), while FusionNet-GRU provided competitive metrics with faster training. The results confirm that the proposed hybrid deep learning and dual-stage feature fusion framework, combined with SMOTE-ENN, offers robust, scalable, and reliable intrusion detection suitable for dynamic cybersecurity environments.
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IJCRT — journal style
"Dual-Stage Data Fusion and Hybrid Deep Learning Framework for Robust Intrusion Detection in Network Traffic", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 3, page no.g146-g163, March 2026, Available :https://ijcrt.org/papers/IJCRT2603740.pdf
APA — 7th edition
Sharma, R. N., Kushwah, J. S., & Yadav, P. (2026). Dual-Stage Data Fusion and Hybrid Deep Learning Framework for Robust Intrusion Detection in Network Traffic. International Journal of Creative Research Thoughts, 14(3), g146-g163. https://doi.org/10.56975/ijcrt.v14i3.303582
MLA — 9th edition
Sharma, Ram Naresh, et al. "Dual-Stage Data Fusion and Hybrid Deep Learning Framework for Robust Intrusion Detection in Network Traffic." International Journal of Creative Research Thoughts, vol. 14, no. 3, 2026, pp. g146-g163, https://doi.org/10.56975/ijcrt.v14i3.303582.
Chicago — 17th, bibliography
Sharma, Ram Naresh, Jitendra Singh Kushwah, and Pritaj Yadav. "Dual-Stage Data Fusion and Hybrid Deep Learning Framework for Robust Intrusion Detection in Network Traffic." International Journal of Creative Research Thoughts 14, no. 3 (2026): g146-g163. https://doi.org/10.56975/ijcrt.v14i3.303582.
Harvard — author–date
Sharma, R.N., Kushwah, J.S. and Yadav, P. (2026) 'Dual-Stage Data Fusion and Hybrid Deep Learning Framework for Robust Intrusion Detection in Network Traffic', International Journal of Creative Research Thoughts, 14(3), pp. g146-g163. Available at: https://doi.org/10.56975/ijcrt.v14i3.303582
IEEE — numbered reference
R. N. Sharma, J. S. Kushwah and P. Yadav, "Dual-Stage Data Fusion and Hybrid Deep Learning Framework for Robust Intrusion Detection in Network Traffic," IJCRT, vol. 14, no. 3, pp. g146-g163, Mar. 2026, doi: 10.56975/ijcrt.v14i3.303582.
Vancouver — biomedical
Sharma RN, Kushwah JS, Yadav P. Dual-Stage Data Fusion and Hybrid Deep Learning Framework for Robust Intrusion Detection in Network Traffic. IJCRT. 2026 Mar;14(3):g146-g163. doi: 10.56975/ijcrt.v14i3.303582
AMA — 11th edition
Sharma RN, Kushwah JS, Yadav P. Dual-Stage Data Fusion and Hybrid Deep Learning Framework for Robust Intrusion Detection in Network Traffic. IJCRT. 2026;14(3):g146-g163. doi:10.56975/ijcrt.v14i3.303582
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2603740,
author = {Ram Naresh Sharma and Jitendra Singh Kushwah and Pritaj Yadav},
title = {Dual-Stage Data Fusion and Hybrid Deep Learning Framework for Robust Intrusion Detection in Network Traffic},
journal = {International Journal of Creative Research Thoughts},
volume = {14},
number = {3},
pages = {g146-g163},
year = {2026},
month = {March},
issn = {2320-2882},
doi = {10.56975/ijcrt.v14i3.303582},
url = {https://doi.org/10.56975/ijcrt.v14i3.303582}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Sharma, Ram Naresh
AU - Kushwah, Jitendra Singh
AU - Yadav, Pritaj
TI - Dual-Stage Data Fusion and Hybrid Deep Learning Framework for Robust Intrusion Detection in Network Traffic
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 14
IS - 3
PY - 2026
SN - 2320-2882
DO - 10.56975/ijcrt.v14i3.303582
UR - https://doi.org/10.56975/ijcrt.v14i3.303582
SP - g146-g163
ER -
Issue
Volume 14 Issue 3,
March-2026
Pages : g146-g163
Article Information
- Pages
- g146-g163
- Published
- March 2026
- Registration ID
- IJCRT_303582
- Published Paper ID
- IJCRT2603740
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- Shivpuri, Madhya Pradesh, India
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- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2603740.pdf
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