An Ensemble Learning Approach for Classifying Illicit Transactions in Bitcoin

Abstract

Bitcoin has become a popular method for illegal transactions, such as ransomware payments and money laundering. Detecting these activities within the Bitcoin blockchain is challenging due to the lack of transaction labels and the network's enormous size, allowing bad actors to hide their actions. Previous studies have suggested using unsupervised anomaly detection or supervised and active learning techniques for identifying illicit activity within Bitcoin's network. This paper presents a novel machine-learning methodology that combines feature engineering with supervised learning algorithms to identify illicit transactions in the Bitcoin network. The approach shows promising results in accurately classifying transactions as illicit or legitimate. This method not only provides an efficient solution for detecting unauthorized transactions but also holds significant implications for establishing robust regulatory frameworks for digital currencies.