Cost Sensitive Approach to Ethereum Transactions Fraud Detection using Machine Learning

Abstract

Ethereum has become one of the most popular blockchains in the world ever since its inception. There are now over 207 million Ethereum accounts and more than 6000 blocks are mined every day. Its fame also attracts various kinds of frauds so it's crucial to detect these frauds to keep Ethereum network sustainable and healthy. The main aim of the paper is to compare various fraud detection methods, besides trying to minimize false positives, and finally suggest the model best suitable for the task of fraud detection in Ethereum transactions by using various evaluation metrics.