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E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT

arXiv 2103.16329 ↗First posted 30 Mar 2021cs.NI cs.AI cs.CRcited by 55

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Abstract (arXiv)

This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs). GNNs are a relatively new sub-field of deep neural networks, which can leverage the inherent structure of graph-based data. Training and evaluation data for NIDSs are typically represented as flow records, which can naturally be represented in a graph format. In this paper, we propose E-GraphSAGE, a GNN approach that allows capturing both the edge features of a graph as well as the topological information for network intrusion detection in IoT networks. To the best of our knowledge, our proposal is the first successful, practical, and extensively evaluated approach of applying GNNs on the problem of network intrusion detection for IoT using flow-based data. Our extensive experimental evaluation on four recent NIDS benchmark datasets shows that our approach outperforms the state-of-the-art in terms of key classification metrics, which demonstrates the potential of GNNs in network intrusion detection, and provides motivation for further research.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). Builds on 13 of its 34 references that are in the map. First posted on arXiv 30 Mar 2021.

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13 of 34 references.

References · 13

  1. 2017Inductive Representation Learning on Large Graphscited by 6,284
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  4. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  5. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  6. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  7. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  8. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  9. 2017A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applicationscited by 260
  10. 2018Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Datasetcited by 98
  11. 2020NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systemscited by 45
  12. 2020Automating Botnet Detection with Graph Neural Networkscited by 23
1 more
  1. 2021An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networkscited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 10; 2023: 8; 2024: 15; 2025: 7; 2026: 14.

55 papers.

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  1. 2023Financial Fraud Detection using Quantum Graph Neural Networkscited by 35
  2. 2022Graph Neural Networks in IoT: A Surveycited by 27
  3. 2022Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networkscited by 25
  4. 2023Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networkscited by 16
  5. 2021Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithmscited by 11
  6. 2022AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detectioncited by 11
  7. 2022Graph Neural Network-based Android Malware Classification with Jumping Knowledgecited by 9
  8. 2022On Generalisability of Machine Learning-based Network Intrusion Detection Systemscited by 8
  9. 2024Use of Graph Neural Networks in Aiding Defensive Cyber Operationscited by 7
  10. 2024Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Networkcited by 7
  11. 2022XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensicscited by 6
  12. 2022ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interestcited by 4
38 more
  1. 2024Survey of Graph Neural Network for Internet of Things and NextG Networkscited by 4
  2. 2022Network Intrusion Detection System in a Light Bulbcited by 3
  3. 2023A Comparison of Graph Neural Networks for Malware Classificationcited by 3
  4. 2026Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Surveycited by 3
  5. 2022Investigation and rectification of NIDS datasets and standardized feature set derivation for network attack detection with graph neural networkscited by 2
  6. 2023Efficient Network Representation for GNN-based Intrusion Detectioncited by 2
  7. 2024EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learningcited by 2
  8. 2024PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Securitycited by 2
  9. 2024XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Modelcited by 2
  10. 2024Network Anomaly Traffic Detection via Multi-view Feature Fusioncited by 2
  11. 2024Towards a graph-based foundation model for network traffic analysiscited by 2
  12. 2025Self-Supervised Learning of Graph Representations for Network Intrusion Detectioncited by 2
  13. 2026Q-AGNN: Quantum-Enhanced Attentive Graph Neural Network for Intrusion Detectioncited by 2
  14. 2022AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approachcited by 1
  15. 2023CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on Graphscited by 1
  16. 20233D-IDS: Doubly Disentangled Dynamic Intrusion Detectioncited by 1
  17. 2024X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection Systemcited by 1
  18. 2024Integrating Graph Neural Networks with Scattering Transform for Anomaly Detectioncited by 1
  19. 2024Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Trafficcited by 1
  20. 2025AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoderscited by 1
  21. 2026Content-Aware Attack Detection in LLM Agent Tool-Call Traffic: An Empirical Study of Features, Architectures, and Evaluation Protocolscited by 1
  22. 2026Let Relations Speak: An End-to-End LLM-GNN Soft Prompt Framework for Fraud Detectioncited by 1
  23. 2026AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigationcited by 1
  24. 2023BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detectioncited by 0
  25. 2023XFedHunter: An Explainable Federated Learning Framework for Advanced Persistent Threat Detection in SDNcited by 0
  26. 2024Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networkscited by 0
  27. 2024BernGraph: Probabilistic Graph Neural Networks for EHR-based Medication Recommendationscited by 0
  28. 2024NIDS Neural Networks Using Sliding Time Window Data Processing with Trainable Activations and its Generalization Capabilitycited by 0
  29. 2024LogSHIELD: A Graph-based Real-time Anomaly Detection Framework using Frequency Analysiscited by 0
  30. 2025CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networkscited by 0
  31. 2025GPML: Graph Processing for Machine Learningcited by 0
  32. 2025Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysiscited by 0
  33. 2025GTCN-G: A Residual Graph-Temporal Fusion Network for Imbalanced Intrusion Detectioncited by 0
  34. 2025Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detectioncited by 0
  35. 2026Pro-ZD: A Transferable Graph Neural Network Approach for Proactive Zero-Day Threats Mitigationcited by 0
  36. 2026MalMoE: Mixture-of-Experts Enhanced Encrypted Malicious Traffic Detection Under Graph Driftcited by 0
  37. 2026MI2DAS: A Multi-Layer Intrusion Detection Framework with Incremental Learning for Securing Industrial IoT Networkscited by 0
  38. 2026Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networkscited by 0

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