stručni rad
Abstract—The increased adoption of cryptocurrency poses
new challenges in the battle against illicit activities on
blockchain-based networks, especially in the Bitcoin network.
Digital forensics has been pivotal in finding and analyzing
different forms of illicit activities in blockchain networks
and consequently provides useful tools for combating crimes
associated with cryptocurrencies. The application of machine
learning offers a promising avenue to automate and accelerate
these forensic procedures, enabling faster and more
accurate identification of illicit activities within blockchain
networks. This paper addresses the problem of illicit Bitcoin
transactions detection using machine learning algorithms on
the Elliptic++ dataset, which is the largest labeled Bitcoin
transaction dataset publicly available, to classify transactions
for any illegal actions. Through feature selection and hyperparameter
tuning, the performance of classifiers such as Logistic
Regression, Random Forest, Multilayer Perceptron, XGBoost,
and their ensemble combinations has been systematically
evaluated and compared. Demonstrating high classification
accuracy, these models can be deemed effective in detecting
fraud on the Bitcoin network and helping forensic experts in
advancing procedures within blockchain forensics.
digital forensics, blockchain, machine learning
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