mlsanityData to 7 Oct 2026

XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics

arXiv 2207.09088 ↗First posted 19 Jul 2022cs.CR cs.LG cs.NIcited by 6

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Abstract (arXiv)

In this paper, we propose XG-BoT, an explainable deep graph neural network model for botnet node detection. The proposed model comprises a botnet detector and an explainer for automatic forensics. The XG-BoT detector can effectively detect malicious botnet nodes in large-scale networks. Specifically, it utilizes a grouped reversible residual connection with a graph isomorphism network to learn expressive node representations from botnet communication graphs. The explainer, based on the GNNExplainer and saliency map in XG-BoT, can perform automatic network forensics by highlighting suspicious network flows and related botnet nodes. We evaluated XG-BoT using real-world, large-scale botnet network graph datasets. Overall, XG-BoT outperforms state-of-the-art approaches in terms of key evaluation metrics. Additionally, we demonstrate that the XG-BoT explainers can generate useful explanations for automatic network forensics.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 18 of its 39 references that are in the map. First posted on arXiv 19 Jul 2022.

Builds on

18 of 39 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2018How Powerful are Graph Neural Networks?cited by 4,355
  3. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  4. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  5. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  6. 2018Sanity Checks for Saliency Mapscited by 1,149
  7. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  8. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  9. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  10. 2020Towards Deeper Graph Neural Networkscited by 296
  11. 2017The Reversible Residual Network: Backpropagation Without Storing Activationscited by 215
  12. 2021Training Graph Neural Networks with 1000 Layerscited by 169
6 more
  1. 2018N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoderscited by 67
  2. 2021E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTcited by 55
  3. 2020Automating Botnet Detection with Graph Neural Networkscited by 23
  4. 2022SCGC : Self-Supervised Contrastive Graph Clusteringcited by 13
  5. 2019A Graph-Based Machine Learning Approach for Bot Detectioncited by 4
  6. 2021Edge-Level Explanations for Graph Neural Networks by Extending Explainability Methods for Convolutional Neural Networkscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 1; 2026: 1.

6 papers.

  1. 2023A Survey on Explainability of Graph Neural Networkscited by 28
  2. 2024Survey of Graph Neural Network for Internet of Things and NextG Networkscited by 4
  3. 2024X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection Systemcited by 1
  4. 2024CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networkscited by 0
  5. 2025ARM-Explainer -- Explaining and improving graph neural network predictions for the maximum clique problem using node features and association rule miningcited by 0
  6. 2026MambaNetBurst: Direct Byte-level Network Traffic Classification without Tokenization or Pretrainingcited by 0