Skip to main content
SpringerLink
Log in

Discriminative boundary generation for effective outlier detection

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Outlier detection is often considered a challenge due to the inherent class imbalance in datasets, with the small number of available outliers that are insufficient to describe their overall distribution. This makes it difficult for classifiers to effectively learn the demarcation (boundary) between normal samples and outliers, which is the key for accurate detection. In this paper, we propose a novel discriminative boundary generation framework, called BoG. The framework extracts the border samples in the dataset and expands them to form the initial boundary outliers. With the adversarial training in GAN, the boundary outliers are further augmented, which, together with the boundary normal data, provides the valuable demarcation information for the classifier. Two method variants are proposed under our BoG framework to achieve a balance between detection efficiency and effectiveness. Extensive experiments show that our proposed framework achieves significant improvements compared to the existing outlier detection methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Fiore U, De Santis A, Perla F, Zanetti P, Palmieri F (2019) Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Inf Sci 479:448–455

    Article  Google Scholar 

  2. Dal Pozzolo A, Boracchi G, Caelen O, Alippi C, Bontempi G (2017) Credit card fraud detection: a realistic modeling and a novel learning strategy. IEEE Trans Neural Netw Learn Syst 29(8):3784–3797

    Google Scholar 

  3. Makki S, Assaghir Z, Taher Y, Haque R, Hacid M-S, Zeineddine H (2019) An experimental study with imbalanced classification approaches for credit card fraud detection. IEEE Access 7:93010–93022

    Article  Google Scholar 

  4. Van Vlasselaer V, Bravo C, Caelen O, Eliassi-Rad T, Akoglu L, Snoeck M, Baesens B (2015) APATE: a novel approach for automated credit card transaction fraud detection using network-based extensions. Decis Support Syst 75:38–48

    Article  Google Scholar 

  5. Choi H, Kim M, Lee G, Kim W (2019) Unsupervised learning approach for network intrusion detection system using autoencoders. J Supercomput 75(9):5597–5621

    Article  Google Scholar 

  6. Osada G, Omote K, Nishide T (2017) Network intrusion detection based on semi-supervised variational auto-encoder. In: European symposium on research in computer security, pp 344–361

  7. Kuypers MA, Maillart T, Paté-Cornell E (2016) An empirical analysis of cyber security incidents at a large organization. In: Department of management science and engineering. Stanford University, School of Information, UC Berkeley, pp 30

  8. Schlegl T, Seeböck P, Waldstein SM, Schmidt-Erfurth U, Langs G (2017) Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: International conference on information processing in medical imaging, pp 146–157

  9. Steinwart I, Hush D, Scovel C (2005) A classification framework for anomaly detection. J Mach Learn Res 6(2):211–232

    MathSciNet  Google Scholar 

  10. Chehreghani MH (2016) K-nearest neighbor search and outlier detection via minimax distances. In: Proceedings of the 2016 SIAM international conference on data mining, pp 405–413

  11. Djenouri Y, Belhadi A, Lin JC-W, Cano A (2019) Adapted k-nearest neighbors for detecting anomalies on spatio-temporal traffic flow. IEEE Access 7:10015–10027

    Article  Google Scholar 

  12. Zhang Y-L, Li L, Zhou J, Li X, Zhou Z-H (2018) Anomaly detection with partially observed anomalies. In: Companion proceedings of the the web conference 2018, pp 639–646

  13. Daneshpazhouh A, Sami A (2014) Entropy-based outlier detection using semi-supervised approach with few positive examples. Pattern Recognit Lett 49:77–84

    Article  Google Scholar 

  14. Daneshpazhouh A, Sami A (2013) Semi-supervised outlier detection with only positive and unlabeled data based on fuzzy clustering. In: The 5th Conference on information and knowledge technology, pp 344–348

  15. Kołcz A, Chowdhury A, Alspector J (2003) Data duplication: An imbalance problem?

  16. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

    Article  Google Scholar 

  17. Han H, Wang W-Y, Mao B-H (2005) Borderline-smote: a new over-sampling method in imbalanced data sets learning. In: International conference on intelligent computing, pp 878–887

  18. He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International joint conference on neural networks (IEEE world congress on computational intelligence), pp 1322–1328

  19. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27

  20. Liu Y, Li Z, Zhou C, Jiang Y, Sun J, Wang M, He X (2019) Generative adversarial active learning for unsupervised outlier detection. IEEE Trans Knowl Data Eng 32(8):1517–1528

    Google Scholar 

  21. Shen H, Chen J, Wang R, Zhang J (2020) Counterfeit anomaly using generative adversarial network for anomaly detection. IEEE Access 8:133051–133062

    Article  Google Scholar 

  22. Ngo PC, Winarto AA, Kou CKL, Park S, Akram F, Lee HK (2019) Fence GAN: towards better anomaly detection. In: 2019 IEEE 31St International conference on tools with artificial intelligence (ICTAI), pp 141–148

  23. Schulze J-P, Sperl P, Böttinger K (2021) Double-adversarial activation anomaly detection: adversarial autoencoders are anomaly generators. arXiv preprint arXiv:2101.04645

  24. Averbuch-Elor H, Bar N, Cohen-Or D (2019) Border-peeling clustering. IEEE Trans Pattern Anal Mach Intell 42(7):1791–1797

    Article  Google Scholar 

  25. Lim SK, Loo Y, Tran N-T, Cheung N-M, Roig G, Elovici Y (2018) Doping: generative data augmentation for unsupervised anomaly detection with GAN. In: 2018 IEEE International conference on data mining (ICDM), pp 1122–1127

  26. Intrator Y, Katz G, Shabtai A (2018) Mdgan: boosting anomaly detection using multi-discriminator generative adversarial networks. arXiv preprint arXiv:1810.05221

  27. Wang X, Du Y, Lin S, Cui P, Shen Y, Yang Y (2020) adVAE: a self-adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection. Knowl Based Syst 190:105187

    Article  Google Scholar 

  28. Borghesi A, Bartolini A, Lombardi M, Milano M, Benini L (2019) Anomaly detection using autoencoders in high performance computing systems. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 9428–9433

  29. Akcay S, Atapour-Abarghouei A, Breckon TP (2018) GANomaly: semi-supervised anomaly detection via adversarial training

  30. Salem M, Taheri S, Yuan JS (2018) Anomaly generation using generative adversarial networks in host-based intrusion detection. In: 2018 9th IEEE Annual Ubiquitous computing, electronics & mobile communication conference (UEMCON), pp 683–687

  31. Schlkopf B, Williamson RC, Smola AJ, Shawe-Taylor J, Platt JC (1999) Support vector method for novelty detection. In: Advances in neural information processing systems 12, NIPS conference, Denver, Colorado, USA, November 29—December 4, 1999

  32. Li L, Huang L, Yang W, Yao X, Liu A (2015) Privacy-preserving LOF outlier detection. Knowl Inf Syst 42(3):579–597

    Article  Google Scholar 

  33. Cheng Z, Zou C, Dong J (2019) Outlier detection using isolation forest and local outlier factor. In: Proceedings of the conference on research in adaptive and convergent systems, pp 161–168

  34. Zhou C, Paffenroth RC (2017) Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 665–674

  35. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: International conference on machine learning, pp 214–223

Download references

Author information

Authors and Affiliations

  1. North University of China, Taiyuan, China

    Ji Zhang

  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Qiliang Liang

  3. Zhejiang Lab, Hangzhou, China

    Mohamed Jaward Bah

  4. Guilin University of Electronic Technology, Guilin, China

    Hongzhou Li & Liang Chang

  5. The University of Aizu, Aizuwakamatsu, Japan

    Rage Uday Kiran

Authors
  1. Ji Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiliang Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Jaward Bah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongzhou Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Liang Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rage Uday Kiran
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

QL, JZ, MJB wrote the main manuscript text, and all authors reviewed the manuscript.

Corresponding authors

Correspondence to Qiliang Liang or Hongzhou Li.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Liang, Q., Bah, M.J. et al. Discriminative boundary generation for effective outlier detection. Knowl Inf Syst 66, 2987–3004 (2024). https://doi.org/10.1007/s10115-023-02012-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-023-02012-3

Keywords

Search

Navigation