Analyzing Various Machine Learning Algorithms for Blockchain-Based Fraud Detection

S. Giribabu; V. Sriharsha; Dr. Patan Hussain Basha; K. Suresh; M. Sivudu

doi:10.55524/ijircst.2022.10.3.59

Abstract

A blockchain network's economics and user confidence can be seriously harmed by fraud. Consensus algorithms like proof of work and proof of stake can verify the legitimacy of a transaction but not the identity of the people who are conducting or verifying it. On a blockchain network, fraud can still occur, as a result of this. One approach to fighting fraud is to make use of machine learning techniques. There are two types of machine learning: supervised and unsupervised. We use a variety of supervised machine learning techniques in this study to distinguish between legitimate and fraudulent purchases. We also compare decision trees, Naive Bayes, logistic regression, multilayer perceptron, and other supervised machine learning techniques in detail for this challenge.

Keywords

Blockchain, Machine, Learning, Machine Learning Algorithms, Fraud Detection

References

Cai, Y., Zhu, D. Fraud detections for online businesses: a perspectivefrom blockchain technology.
Xu, J.J. Are blockchains immune to all malicious attacks
Ostapowicz M., Ë™ Zbikowski K. (2019) Detecting Fraudulent Accounts on Blockchain: A Supervised Approach
Podgorelec, B., Turkanovi´c, M. and KarakatiË‡c, S., 2020. A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection.
Farrugia S, Ellul J, Azzopardi G. Detection of illicit accounts over the Ethereum blockchain. Expert Systems with Applications
Pham, Thai, and Steven Lee. ”Anomaly detection in bitcoin network using unsupervised learning methods
Li, Ji, et al. ”A Survey on Blockchain Anomaly Detection UsingData Mining Techniques.
Brenig, Christian, and G¨unterM¨uller. ”Economic analysis of cryptocurrency backed money laundering.” (2015)
Monamo, Patrick, VukosiMarivate, and Bheki Twala. ”Unsupervised learning for robust Bitcoin fraud detection.” 2016 Information Security for South Africa (ISSA). IEEE, 2016.
Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods
Data mining for credit card fraud: A comparative study S. Bhattacharyya, Sanjeev Jha, K. Tharakunnel, J. Westland
The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature E. Ngai, Yong Hu, Y. Wong, Yijun Chen, Xin Sun
Graph based anomaly detection and description: a survey L. Akoglu, Hanghang Tong, Danai Koutra.

Cites this article as

S. Giribabu, V. Sriharsha, D. P. H. Basha, K. Suresh, M. Sivudu, "Analyzing Various Machine Learning Algorithms for Blockchain-Based Fraud Detection", International Journal of Innovative Research in Engineering & Management (ijircst), Vol-10, Issue-3, Page No-375-380, 2022. Available from: https://doi.org/10.55524/ijircst.2022.10.3.59

Corresponding Author

S. Giribabu

Assistant Professor, Department of Computer Science & Engineering, PACE Institute of Technology and Sciences, Ongole, Andhra Pradesh, India

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