Jul 2026· Journal of Signal Processing Systems· Vol 98· 0 citations· 33 references
Computer Science
TL;DR
GIC-IDS is proposed, a unified intrusion detection framework that integrates graph structure learning, information bottleneck, and contrastive learning that consistently outperforms representative baseline methods in terms of detection accuracy, robustness, and generalization capability.
TAE-MAGSAGE, an edge-centric graph learning model which uses the observed communication structure to construct graphs and applies a line graph transformation to perform flow-level classification without collapsing the interaction relationships, is introduced.
Poonam Bala Nehru, Yun-Peng Zhang, Ren-Jie Hu et al.· International Conference on...· 0 citations
A lightweight intrusion detection model based on Class-Aware Self-Knowledge Distillation (CASKD) that significantly reduces computational overhead while improving detection precision for rare attacks, achieving an optimal balance between model compactness and performance.
Cheng-Hao Liu· Poster Volume 0008 The 2026...· 0 citations
MaGOS-IDS is proposed, which extracts topology-aware embeddings with an edge-aware GCN that fuses flow-level edge features directly into message passing, whitens each class with a regularized Mahalanobis distance so the reject decision respects per-class variance and correlation, and calibrates a per-class EVT tail on...
Thanh T. Nguyen, Minho Park· Electronics· 0 citations
Temporal Graph Neural Networks (TGNNs) have been increasingly applied to network intrusion detection (NID), with some studies reporting accuracy exceeding 99%. This paper argues that such performance can be an artifact of dataset construction flaws rather than genuine model capability. We conduct an empirical audit of...
Yin-Ning Zhang, Sait Suer, S. M. T. F. A. Chowdhoury et al.· 2026 International Conferenc...· 0 citations
An explainable deep learning framework evaluated across multiple heterogeneous cyber attack datasets, including Kitsune, Server-Based network data,enterprise logs, and Malware Traffic datasets, demonstrating the effectiveness of the proposed framework in handling heterogeneous network traffic while providing interpreta...
An explainable network intrusion detection framework that integrates Mutual Information-based feature selection, a stacking ensemble classifier, and SHapley Additive exPlanations (SHAP) that provides both global and local interpretations of model decisions is proposed.
Anjali Singh· Applied Science and Engineer...· 0 citations
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