Detection of Suspicious Activities on the Selected Blockchain Network

Abstract

The emergence of blockchain technology has revolutionized various sectors by introducing decentralized and immutable ledgers. Ethereum, one of the leading blockchain platforms, has gained significant attention for its smart contract capabilities and ability to produce decentralized applications. Despite these innovative advancements, blockchain transaction networks, including Ethereum, have become a new space for the emergence of illegal activities from financial fraud to money laundering and terrorist financing. By combining theoretical knowledge with practical methodologies, this paper aims to contribute to the ongoing efforts to increase the security and transparency of blockchain networks, especially the Ethereum blockchain. The proposed methods leverage supervised machine learning, Financial Action Task Force (FATF) red flags and transaction graph visualization. Through the development and validation of innovative detection methods, the findings of this research are expected to enable stakeholders to proactively mitigate the risks associated with illicit transaction activities on the Ethereum blockchain.