Navigating the Trust Landscape: Fraud Analysis of Ethereum Blockchain Networks Using A.I

Abstract

In the contemporary era marked by the proliferation of digital transactions, ensuring their integrity and authenticity is paramount. This research embarks on an analytical journey into the Ethereum blockchain network, employing machine learning algorithms to scrutinize dataset transactions for fraud detection. The study provides an overview of blockchain networks, elucidating various types of fraud and exploring diverse anomaly detection algorithms. A detailed investigation into transaction-related fields is conducted using a correlation matrix, aiming to construct a robust model that can accurately identify fraudulent transactions within the Ethereum network. Two distinct machine learning algorithms, the Isolation Forest model and the Support Vector Machine (SVM) algorithm, are employed on the Ethereum Fraud Detection dataset sourced from Kaggle. Preliminary validation results are promising, with the Isolation Forest model achieving a 75% accuracy rate in fraud detection and the SVM algorithm demonstrating an exceptional 99.95% accuracy. This research contributes significantly to the field of fraud analysis in blockchain networks. It underscores blockchain technology's potential to bolster social trust by enhancing digital transactions’ security, transparency, and integrity. The findings of this research pave the way for developing and implementing innovative, blockchain-based solutions for fraud prevention and detection, ultimately contributing to enhancing social trust and fortifying various sectors against fraudulent activities.