Detecção de Fraudes em Criptomoedas utilizando Métodos de Classificação de Séries Temporais baseados em Redes Neurais

• Luiz Alfredo Zenon da Mata Caffé Marinha do Brasil
• Rogério Zupo Braga ITA
• Lourenço Alves Pereira Júnior ITA
• Cecilia de Azevedo Castro Cesar ITA
• Cesar Augusto Cavalheiro Marcondes ITA

DOI: https://doi.org/10.5753/sbseg.2023.232860

Abstract

This article addresses the challenge of detecting fraud in cryptocurrencies that originate from an Initial Coin Offering (ICO), which accounts for an estimated 78% of fraudulent activity in the cryptocurrency market. By developing five normalized time series based on the transaction tables of collected cryptocurrencies and analyzing the behavior of fraudulent and non-fraudulent cryptocurrencies, this study employs three types of Artificial Neural Networks (Multilayer Perceptron, Convolution Neural Network-Multilayer Perceptron, and Long Short Term Memory-Multilayer Perceptron) for classification. The results show that the proposed method achieves a Recall of 91% for time samples of 20 days after the cryptocurrency is launched on the market.

References

Adhami, S., Giudici, G., and Martinazzi, S. (2018). Why do businesses go crypto? an empirical analysis of initial coin offerings. Journal of Economics and Business, 100:64– 75.

Bartoletti, M., Carta, S., Cimoli, T., and Saia, R. (2020). Dissecting ponzi schemes on ethereum: identification, analysis, and impact. Future Generation Computer Systems, 102:259–277.

Belitski, M. and Boreiko, D. (2021). Success factors of initial coin offerings. J Technol Transf.

Bellavitis, C., Fisch, C., and Wiklund, J. (2021). A comprehensive review of the global development of initial coin offerings (icos) and their regulation. Journal of Business Venturing Insights, 15:e00213.

Brownlee, J. (2018). Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery.

Campino, J., Brochado, A., and Rosa, A. (2022). Initial coin offerings (icos): Why do they succeed? Financ Innov, 8:17.

Casino, F., Dasaklis, T. K., and Patsakis, C. (2018). A systematic literature review of blockchain-based applications: current status, classification and open issues. Telematics and Informatics, 35(8):2337–2357.

Chen, W., Li, X., Sun, Y., Huang, N., Wang, H., Wu, L., and Liu, X. (2021). Sadponzi: Detecting and characterizing ponzi schemes in ethereum smart contracts. Proc. ACM Meas. Anal. Comput. Syst., 5(2):26.

Chen, W., Zheng, Z., Cui, J., Ngai, E., Zheng, P., and Zhou, Y. (2018). Detecting ponzi schemes on ethereum: Towards healthier blockchain technology. In Proceedings of the 2018 World Wide Web Conference, pages 1409–1418. ACM.

Chen, W., Zheng, Z., Ngai, E., Zheng, P., and Zhou, Y. (2019). Exploiting blockchain data to detect smart ponzi schemes on ethereum. IEEE Access, 7:37575–37586.

Chod, J. and Lyandres, E. (2019). A theory of icos: Diversification, agency, and information asymmetry. Agency, and Information Asymmetry.

Fan, S., Fu, S., Xu, H., and Cheng, X. (2021). Al-spsd: Anti-leakage smart ponzi schemes detection in blockchain. Information Processing & Management, 58(4):102587.

Hartmann, F., Wang, S., and Lunesu, M. (2018). Evaluation of initial cryptoasset offerings: The state of the practice. In 2018 International Workshop on Blockchain Oriented Software Engineering (IWBOSE), pages 33–39. IEEE.

Jung, E., Le Tilly, M., Gehani, A., and Ge, Y. (2019). Data mining-based ethereum fraud detection. In 2019 IEEE International Conference on Blockchain (Blockchain), pages 266–273. IEEE.

Kamps, J. and Kleinberg, B. (2018). To the moon: defining and detecting cryptocurrency pump-and-dumps. Crime Science, 7(1):18.

Kher, R., Terjesen, S., and Liu, C. (2020). Blockchain, bitcoin, and icos: a review and research agenda. Small Business Economics, pages 1–22.

Kiffer, L., Levin, D., and Mislove, A. (2018). Analyzing ethereum’s contract topology. In Proceedings of the Internet Measurement Conference 2018, pages 494–499. ACM.

Milne, A. (2018). Cryptocurrencies from an austrian perspective. In Banking and Monetary Policy from the Perspective of Austrian Economics, pages 223–257. Springer.

Oliva, G. A., Hassan, A. E., and Jiang, Z. M. (2020). An exploratory study of smart contracts in the ethereum blockchain platform. Empirical Software Engineering, pages 1–41.

Petersen, K., Vakkalanka, S., and Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64:1–18.

Szabo, N. (1997). Formalizing and securing relationships on public networks. First Monday.

Thies, F., Wallbach, S., Wessel, M., and Benlian, A. (2021). Initial coin offerings and the cryptocurrency hype the moderating role of exogenous and endogenous signals. Electron Markets.

Ulrich, F. (2017). Bitcoin: a moeda na era digital. LVM Editora, S.l.

Wang, L., Cheng, H., Zibin, Z., Aijun, Y., and Xiaohu, Z. (2021). Ponzi scheme detection via oversampling-based long short-term memory for smart contracts. Knowledge-Based Systems, 228:107312.

Xu, J. and Livshits, B. (2019). The anatomy of a cryptocurrency pump-and-dump scheme. In 28th USENIX Security Symposium (USENIX Security 19), pages 1609–1625.