A HYBRID ANALYTICAL FRAMEWORK FOR BITCOIN TRANSACTION NETWORK FORENSIC INVESTIGATION

Abstract

A hybrid analytical framework is developed for the forensic investigation of Bitcoin transaction networks, addressing the inherent challenges posed by the decentralized and pseudo-anonymous characteristics of blockchain systems. While Bitcoin transactions are publicly accessible, detecting illicit activities within complex transaction graphs remains a significant challenge. Existing approaches typically depend on isolated techniques, such as rule-based methods or standalone machine learning models, which often lack sufficient effectiveness.The proposed framework combines graph-based network analysis, statistical modeling, and machine learning to enhance detection capability. Transactions are represented as a directed graph, where wallet addresses function as nodes and transactions as edges. From this representation, structural, behavioral, and temporal features are systematically extracted and integrated into a unified dataset. A Random Forest classifier is subsequently employed to categorize wallet addresses as either normal or suspicious.This integrated approach improves accuracy, scalability, and robustness, facilitating efficient analysis of large-scale blockchain data and enabling more reliable identification of fraudulent activities in real-world forensic investigations.