Advertisement

Detecting Fraudulent Accounts on Blockchain: A Supervised Approach

  • Michał OstapowiczEmail author
  • Kamil ŻbikowskiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11881)

Abstract

Applications of blockchain technologies got a lot of attention in recent years. They exceed beyond exchanging value and being a substitute for fiat money and traditional banking system. Nevertheless, being able to exchange value on a blockchain is at the core of the entire system and has to be reliable. Blockchains have built-in mechanisms that guarantee whole system’s consistency and reliability. However, malicious actors can still try to steal money by applying well known techniques like malware software or fake emails. In this paper we apply supervised learning techniques to detect fraudulent accounts on Ethereum blockchain. We compare capabilities of Random Forests, Support Vector Machines and XGBoost classifiers to identify such accounts basing on a dataset of more than 300 thousands accounts. Results show that we are able to achieve recall and precision values allowing for the designed system to be applicable as an anti-fraud rule for digital wallets or currency exchanges. We also present sensitivity analysis to show how presented models depend on particular feature and how lack of some of them will affect the overall system performance.

Keywords

Blockchain Anti-fraud Supervised Xgboost Random forests SVM Ethereum 

References

  1. 1.
    Abdallah, A., Maarof, M.A., Zainal, A.: Fraud detection system: a survey. J. Netw. Comput. Appl. 68, 90–113 (2016)CrossRefGoogle Scholar
  2. 2.
    Bhardwaj, A., Gupta, R.: Financial frauds: data mining based detection-a comprehensive survey. Int. J. Comput. Appl. 156(10) (2016) CrossRefGoogle Scholar
  3. 3.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM (1992)Google Scholar
  4. 4.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  5. 5.
    Buterin, V., et al.: A next-generation smart contract and decentralized application platform. White paper (2014)Google Scholar
  6. 6.
    Carneiro, N., Figueira, G., Costa, M.: A data mining based system for credit-card fraud detection in e-tail. Decis. Support Syst. 95, 91–101 (2017).  https://doi.org/10.1016/j.dss.2017.01.002. http://www.sciencedirect.com/science/article/pii/S0167923617300027CrossRefGoogle Scholar
  7. 7.
    Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. CoRR abs/1603.02754 (2016). http://arxiv.org/abs/1603.02754
  8. 8.
    Kou, Y., Lu, C.T., Sirwongwattana, S., Huang, Y.P.: Survey of fraud detection techniques. In: IEEE International Conference on Networking, Sensing and Control, vol. 2, pp. 749–754. IEEE (2004)Google Scholar
  9. 9.
    Pham, T., Lee, S.: Anomaly detection in bitcoin network using unsupervised learning methods. CoRR abs/1611.03941 (2016). http://arxiv.org/abs/1611.03941
  10. 10.
    Quah, J.T., Sriganesh, M.: Real-time credit card fraud detection using computational intelligence. Expert Syst. Appl. 35(4), 1721–1732 (2008).  https://doi.org/10.1016/j.eswa.2007.08.093. http://www.sciencedirect.com/science/article/pii/S0957417407003995CrossRefGoogle Scholar
  11. 11.
    Wood, G., et al.: Ethereum: a secure decentralised generalised transaction ledger. Ethereum Proj. Yellow Pap. 151, 1–32 (2014)Google Scholar
  12. 12.
    Wörner, D., Von Bomhard, T., Schreier, Y.P., Bilgeri, D.: The bitcoin ecosystem: disruption beyond financial services? (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Computer Science, Faculty of Electronics and Information TechnologyWarsaw University of TechnologyWarsawPoland

Personalised recommendations