Rediscovering Fraud Detection in Bitcoin Transactions Using Machine Learning Models

Cryptocurrencies, particularly Bitcoin, have gained considerable attention due to their decentralized nature and potential for high returns. However, they are also subject to fraud-ulent activities, posing challenges to security and transparency. In this paper, we aim to detect fraudulent Bitcoin transactions using machine learning models, including traditional models like Logistic Regression, Decision Trees, and Random Forests, in addition to other deep learning models. Our results demonstrate that when trained on the complete transactional dataset, the Random Forest model outperforms other models, suggesting its potential for effectively detecting fraudulent transactions within the Bitcoin network.