Title: A supervised machine learning-based framework for deanonymisation of Blockchain transactions

Authors: Rohit Saxena; Deepak Arora; Vishal Nagar

Addresses: Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Lucknow Campus, India; Department of Computer Science and Engineering, Pranveer Singh Institute of Technology, Bhauti, Kanpur, India ' Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Lucknow Campus, India ' Department of Computer Science and Engineering, Pranveer Singh Institute of Technology, Bhauti, Kanpur, India

Abstract: As a cryptocurrency, Bitcoin serves as a decentralised ledger for recording transactions. The owner of a Bitcoin keeps their identity secret and hides it behind a special address known as a pseudonym. Because Bitcoin offers anonymity, it has evolved into the favoured option for cybercriminals involved in illegal activities. In this research, supervised machine learning has been used to propose a framework for identifying anonymous user activities on the Blockchain. A labelled dataset containing transactions has been created as a training dataset to carry out the classification of user activities. The fundamental objective is to classify Blockchain transactions to deanonymise them and separate unethical from ethical ones. Synthetic minority oversampling technique (SMOTE) and weight of user activities were used to address the issue of class imbalance. On the samples from the class imbalanced and class balanced datasets, k-nearest neighbours (KNN) exhibited outstanding cross-validation accuracy with default parameters and hyperparameters.

Keywords: Bitcoin; deanonymisation; supervised machine learning; classification; k-nearest neighbours; KNN; decision trees.

DOI: 10.1504/IJESDF.2025.147182

International Journal of Electronic Security and Digital Forensics, 2025 Vol.17 No.4, pp.535 - 561

Received: 07 Nov 2023
Accepted: 17 Jan 2024
Published online: 11 Jul 2025 *

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