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

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.