Classifying blockchain cybercriminal transactions using hyperparameter tuned supervised machine learning models
by Rohit Saxena; Deepak Arora; Vishal Nagar
International Journal of Computational Science and Engineering (IJCSE), Vol. 26, No. 6, 2023

Abstract: Bitcoin is a crypto asset with transactions recorded on a decentralised, publicly accessible ledger. The real-world identity of the bitcoin blockchain users is masked behind a pseudonym, known as an address that provides a high level of anonymity, which is one of the reasons for its widespread use in criminal operations such as ransomware attacks, gambling, etc. As a result, the classification of diverse cybercriminal users' activities and addresses in the bitcoin blockchain is demanded. This research work presents a classification of user activities and addresses associated with illicit transactions using supervised machine learning (ML). The labelled dataset samples are trained using decision trees, ensemble, Bayesian, and instance-based learning. Extra Trees emerged as the best classification model, whereas Gaussian naïve Bayes as the worst. GridSearchCV is employed to optimise the CV accuracy of classification models with CV accuracy below 85% which led to an improvement in the CV accuracy.

Online publication date: Mon, 04-Dec-2023

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