Skip to main content

Blockchain Abnormal Transaction Behavior Analysis: a Survey

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1490))

Abstract

Blockchain technology has been known to the public since cryptocurrencies such as Bitcoin were introduced. While blockchain technology is being developed and used by researchers, the technology is also used to conduct abnormal transaction behaviors, such as money laundering and fraud. After understanding a large amount of research data, this paper summarizes and analyzes the abnormal trading behaviors in blockchains from the basic characteristics of smart contracts and the topology of blockchain networks. These works provide a reference direction for future researchers.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
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

Learn about institutional subscriptions

References

  1. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2009). http://bitcoin.org/bitcoin.pdf

  2. Hu, T., Liu, X., Chen, T., Zhang, X., Huang, X., Niu, W., et al.: Transaction-based classification and detection approach for Ethereum smart contract. Inf. Process. Manage. 58(2)(2021), Article 102462

    Google Scholar 

  3. Norvill, R., Pontiveros, B.B.F., State, R., Awan, I., Cullen, A.: Automated labeling of unknown contracts in ethereum. In: Proceedings of the 26th International Conference on Computer Communications and Networks (ICCCN), pp. 1–6 (2017)

    Google Scholar 

  4. Tian, G., Wang, Q., Zhao, Y., Guo, L., Sun, Z., Lv, L.: Smart contract classification with a bi-LSTM based approach. IEEE Access 8, 43806–43816 (2020)

    Article  Google Scholar 

  5. Bartoletti, M., Pes, B., Serusi, S.: Data mining for detecting bitcoin Ponzi schemes. In: Proceedings of Crypto Valley Conference Blockchain Technology (CVCBT), pp. 75–84, June 2018

    Google Scholar 

  6. Jung, E., Le Tilly, M., Gehani, A., Ge, Y.: Data mining-based Ethereum fraud detection. In: Proceeding of the IEEE International Conference Blockchain (Blockchain), pp. 266–273, July 2019

    Google Scholar 

  7. Chen, W., Zheng, Z., Cui, J., Ngai, E., Zheng, P., Zhou, Y.: Detecting Ponzi schemes on Ethereum: towards healthier blockchain technology. In: Proceedings of World Wide Web Conference (WWW), pp. 1409–1418 (2018)

    Google Scholar 

  8. Pham, T., Lee, S.: Anomaly detection in Bitcoin network using unsupervised learning methods. CoRR, vol. abs/1611.03941 (2016)

    Google Scholar 

  9. Bartoletti, M., Carta, S., Cimoli, T., Saia, R.: Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact (2017). CoRR abs/1703.03779

    Google Scholar 

  10. Scholten, O.J., Hughes, N.G.J., Deterding, S., Drachen, A., Walker, J.A., Zendle, D.I.: Ethereum crypto-games: mechanics, prevalence and gambling similarities. In: Proceedings of the Annual Symposium on Computer-Human Interaction in Play CHI PLAY, Barcelona, Spain, 22–25 October 2019, pp. 379–389 (2019). http://delivery.acm.org/10.1145/3350000/3347178/p379-scholten.pdf

  11. Sheng, M., Sang, A., Zhu, L., et al.: Abnormal transaction behavior recognition based on motivation analysis in blockchain digital currency. J. Comput. Sci. 44(01), 193–208 (2021)

    Google Scholar 

  12. Jiang, L., Zhang, X.: BCOSN: a blockchain-based decentralized online social network. IEEE Trans. Comput. Soc. Syst. 6(6), 1454–1466 (2019)

    Article  Google Scholar 

  13. Xu, Q., Song, Z., Goh, R.S.M., Li, Y.: Building an ethereum and ipfs-based decentralized social network system. In: 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS) (2018)

    Google Scholar 

  14. Meng, J., Fu, F.: Understanding gambling behaviour and risk attitudes using cryptocurrency-based casino blockchain data. R. Soc. Open Sci. 7, 201446 (2020). http://dx.doi.org/10.1098/rsos.201446

  15. Liu, S., Liao, G., Ding, Y.: Stock transaction prediction modelling and analysis based on LSTM. In: 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 2787–2790 (2018)

    Google Scholar 

  16. Duhart, B.M.A., Hernndez-Gress, N.: Review of the principal indicators and data science techniques used for the detection of financial fraud and money laundering. In: 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1397–1398 (2016)

    Google Scholar 

  17. Staderini, M., Palli, C., Bondavalli, A.: Classification of Ethereum Vulnerabilities and their Propagations. In. Second International Conference on Blockchain Computing and Applications (BCCA) 2020, pp. 44–51 (2020)

    Google Scholar 

  18. Bogner, A.: Seeing is understanding: anomaly detection in blockchains with visualized features. In: Proceedings of the International Joint Conference Pervasive Ubiquitous Computing International Symposium on Wearable Computers, pp. 5–8 (2017)

    Google Scholar 

  19. Wang, X., He, J., Xie, Z., Zhao, G., Cheung, S.-C.: ContractGuard: defend ethereum smart contracts with embedded intrusion detection. IEEE Trans. Services Comput. 13(2), 314–328 (2020)

    Google Scholar 

  20. Di Battista, G., Di Donato, V., Patrignani, M., Pizzonia, M., Roselli, V., Tamassia, R.: Bitconeview: visualization of flows in the bitcoin transaction graph. In: 2015 IEEE Symposium on Visualization for Cyber Security (VizSec), Chicago, IL, USA, 2015, pp. 1–8 (2015). https://doi.org/10.1109/VIZSEC.2015.7312773

  21. McGinn, D., Birch, D., Akroyd, D., et al.: Visualizing dynamic bitcoin transaction patterns. Big Data 4(2), 109–119 (2016)

    Article  Google Scholar 

  22. Pham, T., Lee, S.: Anomaly detection in bitcoin network using unsupervised learning methods. arXiv preprint arXiv:1611.03941 (2016)

  23. Di Francesco Maesa, D., Marino, A., Ricci, L.: An analysis of the Bitcoin users graph: inferring unusual behaviours. In: COMPLEX NETWORKS 2016 2016. SCI, vol. 693, pp. 749–760. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-50901-3_59

  24. Maesa, D.D.F., Marino, A., Ricci, L.: Detecting artificial behaviours in the bitcoin users graph. Online Soc. Networks Media 3, 63–74 (2017)

    Article  Google Scholar 

  25. Ron, D., Shamir, A.: Quantitative analysis of the full bitcoin transaction graph. In: Sadeghi, A.-R. (ed.) FC 2013. LNCS, vol. 7859, pp. 6–24. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39884-1_2

    Chapter  Google Scholar 

  26. Mser, M., Bhme, R., Breuker, D.: An inquiry into money laundering tools in the Bitcoin ecosystem. In: 2013 APWG eCrime Researchers Summit, pp. 1–14. IEEE (2013)

    Google Scholar 

  27. Maksutov, A.A., Alexeev, M.S., Fedorova, N.O., Andreev, D.A.: Detection of blockchain transactions used in blockchain mixer of coin join type. In: 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Saint Petersburg and Moscow, Russia, 2019, pp. 274–277 (2019). https://doi.org/10.1109/EIConRus.2019.8656687

  28. Huang, D.Y., et al.: Tracking ransomware end-to-end. In: 2018 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, 2018, pp. 618–631 (2018). https://doi.org/10.1109/SP.2018.00047

  29. Liao, K., Zhao, Z., Doupe, A., Ahn, G.: Behind closed doors: measurement and analysis of CryptoLocker ransoms in Bitcoin. In: 2016 APWG Symposium on Electronic Crime Research (eCrime). Toronto, ON, Canada, 2016, pp. 1–13 (2016). https://doi.org/10.1109/ECRIME.2016.7487938

  30. Paquet-Clouston, M., Haslhofer, B., Dupont, B.: Ransomware payments in the bitcoin ecosystem. J. Cybersecurity 5(1), tyz003 (2019)

    Google Scholar 

  31. Sun, W., Xu, G., Yang, Z., et al.: Early detection of smart ponzi scheme contracts based on behavior forest similarity. In: 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS), pp. 297–309. IEEE (2020)

    Google Scholar 

  32. Chen, W., Wu, J., Zheng, Z., et al.: Market manipulation of bitcoin: evidence from mining the Mt. Gox transaction network. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 964–972. IEEE (2019)

    Google Scholar 

  33. Lee, S., Yoon, C., Kang, H., et al.: Cybercriminal minds: an investigative study of cryptocurrency abuses in the Dark Web. In: NDSS (2019)

    Google Scholar 

Download references

Acknowledgement

This work is supported by the National Key R&D Program of China (No. 2020YFB1006002)

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yin Zhang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Han, H., Chen, Y., Guo, C., Zhang, Y. (2021). Blockchain Abnormal Transaction Behavior Analysis: a Survey. In: Dai, HN., Liu, X., Luo, D.X., Xiao, J., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2021. Communications in Computer and Information Science, vol 1490. Springer, Singapore. https://doi.org/10.1007/978-981-16-7993-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-7993-3_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7992-6

  • Online ISBN: 978-981-16-7993-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics