Skip to main content
SpringerLink
Log in

MSTAN: A Multi-view Spatio-Temporal Aggregation Network Learning Irregular Interval User Activities for Fraud Detection

  • Conference paper
  • First Online:
Advances in Knowledge Discovery and Data Mining (PAKDD 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14649))

Included in the following conference series:

  • 187 Accesses

Abstract

Discovering fraud patterns from numerous user activities is crucial for fraud detection. However, three factors make this task quite challenging: Firstly, previous research usually utilize just one of the two forms of user activity, namely sequential behavior and interaction relationship, leaving much information unused. Additionally, nearly all works merely study on a single view of user activities, but fraud patterns often span across multiple views. Moreover, most existing models can only handle regular time intervals, while in reality, user activities occur with irregular time intervals. To effectively discover fraud patterns from user activities, this paper proposes MSTAN (Multi-view Spatio-Temporal Aggregation Network) for fraud detection. It addresses the above problems through three phases: (1) In short-term aggregation, SIFB (Sequential behavior and Interaction relationship Fusion Block) is employed to integrate sequential behavior and interaction relationship. (2) In view aggregation, 2-dimensional multi-view user activity embedding is obtained for simultaneously mining multiple views. (3) In long-term aggregation CTLSTM (Convolutional Time LSTM) is designed to deal with irregular time intervals. Experiments on two real world datasets demonstrate that our model outperforms the comparison methods.

Supported by Shenzhen Science and Technology Program under Grant No. GXWD20220817124827001 and No. JCYJ20210324132406016.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Guo, J., Liu, G., Zuo, Y.: Learning sequential behavior representations for fraud detection. In: ICDM, pp. 127–136 (2018)

    Google Scholar 

  2. Li, A., Qin, Z., Liu, R.: Learning sequential behavior representations for fraud detection. In: CIKM, pp. 2703–2711 (2019)

    Google Scholar 

  3. Tempelmeier, N., Demidova, E.: Attention-based vandalism detection in OpenStreetMap. In: WWW, pp. 643–651 (2022)

    Google Scholar 

  4. Zhu, F., Zhang, C., Zheng, Z.: Click fraud detection of online advertising-LSH based tensor recovery mechanism. In: TITS, pp. 9747–9754 (2021)

    Google Scholar 

  5. Hooi, B., Song, H.A., Beutel, A.: Fraudar: bounding graph fraud in the face of camouflage. In: SIGKDD, pp. 895–904 (2016)

    Google Scholar 

  6. Hilal, W., Gadsden, S.A., Yawney, J.: Financial fraud: a review of anomaly detection techniques and recent advances. Expert Syst. Appl. 193, 116429 (2022)

    Article  Google Scholar 

  7. Hilal, W., Gadsden, S.A., Yawney, J.: Wikipedia vandal early detection: from user behavior to user embedding. In: ECML PKDD, pp. 832–846 (2017)

    Google Scholar 

  8. Liu, Y., Ao, X., Qin, Z.: Pick and choose: a GNN-based imbalanced learning approach for fraud detection. In: WWW, pp. 3168–3177 (2021)

    Google Scholar 

  9. Peng, Z., Luo, M., Li, J.: A deep multi-view framework for anomaly detection on attributed networks. IEEE TKDE 34(6), 2539–2552 (2020)

    Google Scholar 

  10. Nyajowi, T., Oyie, N., Ahuna, M.: CNN real-time detection of vandalism using a hybrid-LSTM deep learning neural networks. In: IEEE AFRICON, pp. 1–6 (2021)

    Google Scholar 

  11. Benchaji, I., Douzi, S., El Ouahidi, B.: Enhanced credit card fraud detection based on attention mechanism and LSTM deep model. J. Big Data 8, 1–21 (2021)

    Article  Google Scholar 

  12. Velickovic, P., Cucurull, G., Casanova, A.: Graph attention networks. In: ICLR, pp. 1–10 (2017)

    Google Scholar 

  13. Pareja, A., Domeniconi, G., Chen, J.: Evolvegcn: evolving graph convolutional networks for dynamic graphs. In: AAAI, pp. 5363–5370 (2020)

    Google Scholar 

  14. Skarding, J., Gabrys, B., Musial, K.: Foundations and modeling of dynamic networks using dynamic graph neural networks: a survey. IEEE Access 9, 79143–79168 (2021)

    Article  Google Scholar 

  15. Whitrow, C., Hand, D.J., Juszczak, P.: Transaction aggregation as a strategy for credit card fraud detection. DMKD 18, 30–55 (2009)

    MathSciNet  Google Scholar 

  16. Zhao, T., Ni, B., Yu, W.: Action sequence augmentation for early graph-based anomaly detection. In: CIKM, pp. 2668–2678 (2021)

    Google Scholar 

  17. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR, pp. 1–10 (2016)

    Google Scholar 

  18. Zhang, C., Song, D., Huang, C.: Heterogeneous graph neural network. In: SIGKDD, pp. 793–803 (2019)

    Google Scholar 

  19. Cheng, D., Wang, X., Zhang, Y.: Graph neural network for fraud detection via spatial-temporal attention. IEEE TKDE 34(8), 3800–3813 (2020)

    Google Scholar 

  20. Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. JMLR 9(11) (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China

    Wenbo Zhang, Shuo Zhang, Xingbang Hu & Hejiao Huang

  2. Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Shenzhen, China

    Hejiao Huang

Authors
  1. Wenbo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xingbang Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hejiao Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hejiao Huang .

Editor information

Editors and Affiliations

  1. Taipei, Taiwan

    De-Nian Yang

  2. Microsoft Research Asia, Beijing, China

    Xing Xie

  3. National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  4. Duke University, Durham, NC, USA

    Jian Pei

  5. National Cheng Kung University, Tainan, Taiwan

    Jen-Wei Huang

  6. Silesian University of Technology, Gliwice, Poland

    Jerry Chun-Wei Lin

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, W., Zhang, S., Hu, X., Huang, H. (2024). MSTAN: A Multi-view Spatio-Temporal Aggregation Network Learning Irregular Interval User Activities for Fraud Detection. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_31

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-2262-4_31

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2264-8

  • Online ISBN: 978-981-97-2262-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation