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January 1, 2026Journal of Cyber SecurityOpen Access

Domain Knowledge-Guided Training for NIDS: A Class-Agnostic Evaluation of Robustness on Imbalanced Datasets

Zakaria S. M. Abdelhalim Nahla A. Belal Mohamed Seifeldin

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Key Points

This research investigates the effectiveness of domain knowledge-guided training for improving the robustness of network intrusion detection systems on imbalanced datasets.
Evaluation of existing NIDS approaches using deep learning techniques
Analysis of performance on imbalanced datasets
Implementation of domain knowledge-guided training strategies
Enhanced robustness of NIDS against diverse attack scenarios
Improved detection rates in imbalanced settings
Reduced false positive rates compared to traditional methods

Implication

Research evaluates robustness of NIDS on imbalanced datasets, suggesting improvements through domain-guided training.

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Cite This Study

Abdelhalim et al. (2026) studied this question.

synapsesocial.com/papers/69e07c1e2f7e8953b7cbd8f7 https://doi.org/10.32604/jcs.2026.079097

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Design and Implementation of Network Intrusion Detection System ( <scp>NIDS</scp> ) Based on Deep Learning2025
2A Study on Network Intrusion Detection System2024 · 4 citations
3Evaluating and Improving Adversarial Robustness of Deep Learning -Based Network Intrusion Detector2024 · 2 citations
4Network Intrusion Detection System Using Machine Learning and Deep Learning2024 · 5 citations
5NIDS-Vis: Improving the generalized adversarial robustness of network intrusion detection system2024 · 23 citations

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