A Supervised Machine Learning Approach to Deanonymizing the Bitcoin Blockchain

Decentralized Finance (DeFi) is revolutionizing financial systems by eliminating intermediaries and enabling peer-to-peer transactions through blockchain technology. It enhances transparency, security, and accessibility, allowing users to access financial services such as lending, borrowing, and trading without relying on centralized institutions. Predictions for DeFi indicate exponential growth, with the integration of AI and machine learning driving significant advancements in fraud detection, risk assessment, and transaction analysis. Before the integration of AI, financial fraud detection primarily relied on rule-based systems, manual audits, and traditional statistical models—methods that lacked adaptability and real-time decision-making capabilities. Legacy systems such as credit scoring models and transaction monitoring frameworks struggled with scalability and required continuous human intervention. As fraudulent activities and cyber threats have grown more sophisticated, the limitations of traditional solutions have become increasingly apparent, underscoring the need for AI-driven approaches. By leveraging machine learning, transaction patterns can be analyzed with greater accuracy, enabling real-time anomaly detection and significantly reducing financial risk. The motivation behind this development is to enhance security, improve accuracy in transaction classification, and deliver scalable financial crime detection solutions. Conventional fraud detection mechanisms often fail to keep pace with evolving threats, resulting in considerable financial losses. Manual reviews are time-consuming and error-prone, while static models lack adaptability to emerging fraudulent behaviors. Machine learning enables real-time monitoring and predictive analysis, allowing financial institutions to detect suspicious activities with heightened precision. The proposed system integrates a combination of classifiers—including decision trees, logistic regression, AdaBoost, gradient boosting, k-nearest neighbors, and random forest algorithms—to improve transaction classification accuracy. AI-driven analysis enhances fraud detection by learning from historical data, reducing false positives, and enabling automated de-anonymization of transactions. This system applies advanced algorithms to identify fraudulent patterns, optimize financial security, and streamline transaction verification. By automating the process, AI-powered models offer a more robust and efficient approach to securing financial transactions, ensuring greater reliability and trust in decentralized finance.