Analyzing Various Machine Learning Algorithms for Blockchain-Based Fraud Detection

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.