ArticleScientific reports2024
Federated learning for network attack detection using attention-based graph neural networks.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- GLANet: global and local anomaly network for distributed cyber threat detection using FL.Scientific reports · 2026Article
- GNN-based trust propagation and intelligent certificate revocation decision mechanism for large-scale IoT networks.Scientific reports · 2026Article
- An explainable multi-head attention network for healthcare IoT threat detection based on the MedDefender-MHAN framework.PloS one · 2026Article
- GeoGuard-PTI: a geo-temporal predictive threat intelligence framework with spatiotemporal attack forecasting and closed-loop adaptive defense.Frontiers in artificial intelligence · 2026Article
- Secure federated learning with metaheuristic optimized dimensionality reduction and multi-head attention for DDoS attack mitigation.Scientific reports · 2025Article
- Optimizing IoT intrusion detection with cosine similarity based dataset balancing and hybrid deep learning.Scientific reports · 2025Article
- A Petri Net and LSTM Hybrid Approach for Intrusion Detection Systems in Enterprise Networks.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Federated Learning is an effective solution to address the issues of data isolation and privacy leakage in machine learning. However, ensuring the security of network devices and architectures deploying federated learning remains a challenge due to network attacks. This paper proposes an attention-based Graph Neural Network for detecting cross-level and cross-department network attacks. This method enables collaborative model training while protecting data privacy on distributed devices. By organizing network traffic information in chronological order and constructing a graph structure based on log density, enhances the accuracy of network attack detection. The introduction of an attention mechanism and the construction of a Federated Graph Attention Network (FedGAT) model are used to evaluate the interactivity between nodes in the graph, thereby improving the precision of internal network interactions. Experimental results demonstrate that our method achieves comparable accuracy and robustness to traditional detection methods while prioritizing privacy protection and data security.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.