Blockchain Fraud Detection Using Unsupervised Learning: Anomalous Transaction Patterns Detection Using K-Means Clustering

Abstract

In the dynamic and rapidly evolving landscape of blockchain technology, traditional fraud detection methods, which often rely on labeled data, face limitations due to the diverse and adaptive nature of fraud. This study introduces a novel framework that employs the K-Means clustering algorithm, a technique celebrated for its unsupervised learning capabilities, to detect anomalous transaction patterns indicative of potential fraud, such as unusually high transaction volumes or rapid transfers between wallets. By circumventing the need for pre-labeled examples of fraudulent activity, our approach significantly enhances adaptability and applicability across various blockchain contexts. We apply this framework to a comprehensive dataset encompassing multiple cryptocurrencies, including Bitcoin, Ethereum, Doge Coins, and Tether, analyzing attributes such as closing prices, volatility, and market volume. The results demonstrate the framework’s effectiveness in isolating outliers and identifying transactions that bear hallmarks of suspicious activity, thereby contributing a powerful tool for proactive fraud detection. This research not only paves the way for future advancements in blockchain security but also reinforces the trustworthiness and integrity of blockchain systems by providing a robust mechanism for identifying and mitigating fraudulent activities without the constraints of traditional, supervised methods.