mlsanityData to 7 Oct 2026

Graph Mining for Cybersecurity: A Survey

arXiv 2304.00485 ↗First posted 2 Apr 2023cs.CR cs.AI cs.SIcited by 5

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Abstract (arXiv)

The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Securing cyberspace has become an utmost concern for organizations and governments. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. In recent years, with the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance. It is imperative to summarize existing graph-based cybersecurity solutions to provide a guide for future studies. Therefore, as a key contribution of this paper, we provide a comprehensive review of graph mining for cybersecurity, including an overview of cybersecurity tasks, the typical graph mining techniques, and the general process of applying them to cybersecurity, as well as various solutions for different cybersecurity tasks. For each task, we probe into relevant methods and highlight the graph types, graph approaches, and task levels in their modeling. Furthermore, we collect open datasets and toolkits for graph-based cybersecurity. Finally, we outlook the potential directions of this field for future research.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 76 of its 250 references that are in the map. First posted on arXiv 2 Apr 2023.

Builds on

76 of 250 references.

References · 76

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  6. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  7. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  8. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  9. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  10. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  11. 2019Heterogeneous Graph Attention Networkcited by 732
  12. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 583
38 more
  1. 2015LINE: Large-scale Information Network Embeddingcited by 582
  2. 2017Representation Learning on Graphs: Methods and Applicationscited by 456
  3. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  4. 2019Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networkscited by 266
  5. 2017A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applicationscited by 260
  6. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 248
  7. 2020Can Graph Neural Networks Count Substructures?cited by 235
  8. 2017graph2vec: Learning Distributed Representations of Graphscited by 228
  9. 2020Generalization and Representational Limits of Graph Neural Networkscited by 217
  10. 2017struc2vec: Learning Node Representations from Structural Identitycited by 214
  11. 2017Graph Embedding Techniques, Applications, and Performance: A Surveycited by 188
  12. 2014Graph-based Anomaly Detection and Description: A Surveycited by 182
  13. 2019Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensicscited by 158
  14. 2020Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networkscited by 152
  15. 2021Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillationcited by 151
  16. 2020A Semi-supervised Graph Attentive Network for Financial Fraud Detectioncited by 150
  17. 2020DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphscited by 139
  18. 2020A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sourcescited by 126
  19. 2020GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Mediacited by 108
  20. 2020FANG: Leveraging Social Context for Fake News Detection Using Graph Representationcited by 98
  21. 2020Heterogeneous Graph Neural Networks for Malicious Account Detectioncited by 95
  22. 2018GeniePath: Graph Neural Networks with Adaptive Receptive Pathscited by 66
  23. 2017Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detectioncited by 65
  24. 2019Spam Review Detection with Graph Convolutional Networkscited by 57
  25. 2020How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Surveycited by 38
  26. 2021Federated Graph Learning -- A Position Papercited by 34
  27. 2021Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detectioncited by 34
  28. 2018False Information on Web and Social Media: A Surveycited by 33
  29. 2019Network-based Fake News Detection: A Pattern-driven Approachcited by 33
  30. 2019Towards Federated Graph Learning for Collaborative Financial Crimes Detectioncited by 33
  31. 2018Scalable Graph Learning for Anti-Money Laundering: A First Lookcited by 32
  32. 2019Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitationcited by 32
  33. 2017An Attention-based Collaboration Framework for Multi-View Network Representation Learningcited by 31
  34. 2019Jointly embedding the local and global relations of heterogeneous graph for rumor detectioncited by 31
  35. 2018Deep Learning for Encrypted Traffic Classification: An Overviewcited by 27
  36. 2021Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusioncited by 24
  37. 2019T-EDGE: Temporal WEighted MultiDiGraph Embedding for Ethereum Transaction Network Analysiscited by 19
  38. 2020Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networkscited by 19

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2025Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunitiescited by 2
  2. 2026PDFInspect: A Unified Feature Extraction Framework for Malicious Document Detectioncited by 1
  3. 2026AI Native Asset Intelligencecited by 1
  4. 2024Towards Trustworthy Web Attack Detection: An Uncertainty-Aware Ensemble Deep Kernel Learning Modelcited by 0
  5. 2025Heterogeneous Graph Backdoor Attackcited by 0