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.

Published:

07-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1622-1628

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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