Forensic Analysis and Detection of Illicit Transactions in Bitcoin Network

Abstract

Blockchain technology, celebrated for its decentralized architecture and promise of transparency, has also become a conduit for illicit activities. This paper introduces a novel rule-based methodology for detecting illegal transactions within blockchain networks. By analyzing transaction data through parameters such as block range, transaction hashes, and addresses, the approach identifies suspicious patterns including high-value transactions, unusual fee structures, and links to known illicit addresses. Implemented in Python, the methodology has been rigorously tested and validated, achieving an accuracy of $88 \%$ with strong precision, recall, and $F 1$ scores. This research advances the field of cybersecurity, regulatory compliance, and law enforcement by providing a practical and computationally efficient framework for detecting illegal activities in Bitcoin transactions. The rule-based framework, utilizing both threshold-based and heuristic rules, offers valuable tools for financial institutions, law enforcement, and regulators to identify and investigate illicit transactions effectively. The study underscores the need for proactive measures in protecting digital ecosystems and proposes future enhancements, including advanced anomaly detection and realtime analysis, to further improve the framework’s efficacy and adaptability.