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Cited by 5Network Security and Intrusion DetectionAdversarial Robustness in Machine LearningArtificial Immune Systems Applications

The DOI matches its Crossref record.

One-Class Intrusion Detection with Dynamic Graphs

Liuliakov, Aleksei, Schulz, Alexander, Hermes, Luca, Hammer, Barbara·2023·Lecture Notes in Computer Science
DOI 10.1007/978-3-031-44216-2_44
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Crossref · 10.1007/978-3-031-44216-2_44

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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In cybersecurity, dynamic graph modeling is essential for capturing the continuously changing nature of both benign and malicious activities.

    “In the cybersecurity domain, dynamic graph modeling is essential for capturing the continuously changing nature of both benign and malicious activities.”

    From A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection (2025) · cites this paper as [24]

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 23

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  2. Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LoF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104 (2000) · DOI · Paper page
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  5. Kumar, S., Zhang, X., Leskovec, J.: Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1269–1278 (2019) · DOI
  6. Kwon, D., Kim, H., Kim, J., Suh, S.C., Kim, I., Kim, K.J.: A survey of deep learning-based network anomaly detection. Clust. Comput. 22, 949–961 (2019) · DOI
  7. Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422. IEEE (2008) · DOI · Paper page
  8. Liuliakov, A., Hermes, L., Hammer, B.: AutoML technologies for the identification of sparse classification and outlier detection models. Appl. Soft Comput. 133, 109942 (2023) · DOI
  9. Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. (CSUR) 54(2), 1–38 (2021) · DOI · Paper page
  10. Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
  11. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
  12. Poursafaei, F., Huang, S., Pelrine, K., Rabbany, R.: Towards better evaluation for dynamic link prediction (2022) · DOI
  13. Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., Bronstein, M.: Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637 (2020) · arXiv
  14. Ruff, L., et al.: A unifying review of deep and shallow anomaly detection. Proc. IEEE 109(5), 756–795 (2021) · DOI
  15. Ruff, L., et al.: Deep one-class classification. In: International Conference on Machine Learning, pp. 4393–4402. PMLR (2018)
  16. Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001) · DOI · Paper page
  17. Sharafaldin, I., Lashkari, A.H., Ghorbani, A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: ICISSP (2018) · DOI · Paper page
  18. Shen, L., Li, Z., Kwok, J.: Timeseries anomaly detection using temporal hierarchical one-class network. Adv. Neural. Inf. Process. Syst. 33, 13016–13026 (2020)
  19. Tax, D.M., Duin, R.P.: Support vector data description. Mach. Learn. 54, 45–66 (2004) · DOI
  20. Trivedi, R., Farajtabar, M., Biswal, P., Zha, H.: Dyrep: learning representations over dynamic graphs. In: International Conference on Learning Representations (2019)
  21. Wang, Y., Chang, Y.Y., Liu, Y., Leskovec, J., Li, P.: Inductive representation learning in temporal networks via causal anonymous walks. arXiv preprint arXiv:2101.05974 (2021) · arXiv
  22. Wu, Y., Gu, M., Wang, L., Lin, Y., Wang, F., Yang, H.: Event2graph: event-driven bipartite graph for multivariate time-series anomaly detection. arXiv preprint arXiv:2108.06783 (2021) · arXiv
  23. Xu, D., Ruan, C., Korpeoglu, E., Kumar, S., Achan, K.: Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962 (2020) · arXiv

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Metadata

Authors
Liuliakov, A., Schulz, A., Hermes, L., & Hammer, B.
Affiliations
Bielefeld University (DE)
Corresponding author
Liuliakov, Aleksei
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer Nature Switzerland
Published
2023
Pages
537-549
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-031-44216-2_44
OpenAlex
W4386932075
Cited by
5
References
0
Citations by year
2026: 2 · 2025: 2 · 2024: 1
FWCI
1.51
Type
conference paper
Language
en
Open access
closed
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
Keywords
intrusion detection, one-class classification, dynamic graphs, network security, deep anomaly detection, support vector data description, network communication

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