Machine Learning Approach for Cryptocurrency Fraud Detection
Anahtar Kelimeler:
Cryptocurrency- Ethereum- Feature selection- Fraud detection- Machine learningÖzet
Fraud detection is an important aspect of maintaining the security and integrity of the Ethereum blockchain network. Ethereum is a decentralized blockchain platform that has gained widespread adoption for its smart contract functionality. This paper proposes a two-stage machine-learning approach for detecting fraudulent activities on the Ethereum network. In the first stage, we apply SMOTE to balance the dataset, then baseline accuracies are obtained more accurately. In the second stage, we rank the features according to the F_regression algorithm. We evaluate the feature subsets performances of our model on the balanced Kaggle Ethereum fraud detection dataset using logistic regression, Gaussian Naive Bayes, and support vector machine algorithms. Our results show that using the Gaussian Naïve Bayes machine learning method to detect fraud on the Ethereum network gives the highest accuracy.
Referanslar
Andolfatto, D., & Martin, F. M. (2022). The Blockchain Revolution: Decoding Digital Currencies. Federal Reserve Bank of St Louis Review, 104(3), 149-165. doi:10.20955/r.104.149-65
Ashfaq, T., Khalid, R., Yahaya, A., Aslam, S., Azar, A., Alsafari, S., & Hameed, I. (2022). A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism. Sensors, 22(19). doi:10.3390/s22197162
Aziz, R. M., Baluch, M. F., Patel, S., & Ganie, A. H. (2022). LGBM: a machine learning approach for Ethereum fraud detection. International Journal of Information Technology, 1-11.
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., . . . Grobler, J. (2013). API design for machine learning software: experiences from the scikit-learn project. arXiv preprint arXiv:1309.0238.
Buterin, V. (2014). A next-generation smart contract and decentralized application platform. white paper, 3(37), 2-1.
CoinMarketCap (Producer). (2022). Major Cryptoassets By Percentage of Total Market Capitalization (Bitcoin Dominance Chart). Retrieved from https://coinmarketcap.com/charts/
Farrugia, S., Ellul, J., & Azzopardi, G. (2020). Detection of illicit accounts over the Ethereum blockchain. Expert Systems with Applications, 150, 113318.
Gonzalez-Cuautle, D., Hernandez-Suarez, A., Sanchez-Perez, G., Toscano-Medina, L. K., Portillo-Portillo, J., Olivares-Mercado, J., . . . Sandoval-Orozco, A. L. (2020). Synthetic minority oversampling technique for optimizing classification tasks in botnet and intrusion-detection-system datasets. Applied Sciences, 10(3), 794.
Ibrahim, R. F., Elian, A. M., & Ababneh, M. (2021). Illicit account detection in the ethereum blockchain using machine learning. Paper presented at the 2021 International Conference on Information Technology (ICIT).
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., . . . Dubourg, V. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
Sarker, I. (2021). CyberLearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks. Internet of Things, 14. doi:10.1016/j.iot.2021.100393
Sayadi, S., Rejeb, S. B., & Choukair, Z. (2019). Anomaly detection model over blockchain electronic transactions. Paper presented at the 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC).
Schar, F. (2021). Decentralized Finance: On Blockchain- and Smart Contract-Based Financial Markets. Federal Reserve Bank of St Louis Review, 103(2), 153-174. doi:10.20955/r.103.153-74
Wang, M., Zheng, K., Yang, Y., & Wang, X. (2020). An Explainable Machine Learning Framework for Intrusion Detection Systems. IEEE Access, 8, 73127-73141. doi:10.1109/ACCESS.2020.2988359
İndir
Yayınlanmış
Nasıl Atıf Yapılır
Sayı
Bölüm
Lisans
Telif Hakkı (c) 2023 Siber Politikalar Dergisi
Bu çalışma Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License ile lisanslanmıtır.
