Análise Forense aplicada ao Bitcoin

Abstract

This chapter presents comprehensive concepts on forensic analysis applied to Bitcoin through machine learning techniques. The study begins with a theoretical overview of Bitcoin and its ecosystem, detailing the decentralized nature of its blockchain and the challenges associated with its pseudonymous transactions. It then explores methods for acquiring and processing blockchain data, highlighting fundamental statistical analyses that reveal transaction patterns and anomalies. A section is also dedicated to the application of heuristics H1 and H2, which are essential for tracing mixed transactions. Additionally, we examine the concept of OSINT to enrich blockchain data with external intelligence, providing deeper insights into suspicious activities. Finally, the chapter explores the application of supervised and unsupervised machine learning models in Bitcoin forensics. These models are evaluated for their effectiveness in detecting illicit activities, identifying suspicious entities, and improving the accuracy of forensic investigations. The findings underscore the potential of combining machine learning with traditional forensic methods to enhance the overall robustness of Bitcoin investigations.