BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis
Cryptocurrencies are no longer just the preferred option for cybercriminal
activities on darknets, due to the increasing adoption in mainstream
applications. This is partly due to the transparency associated with the
underpinning ledgers, where any individual can access the record of a
transaction record on the public ledger. In this paper, we build a dataset
comprising Bitcoin transactions between 12 July 2019 and 26 May 2021. This
dataset (hereafter referred to as BABD-13) contains 13 types of Bitcoin
addresses, 5 categories of indicators with 148 features, and 544,462 labeled
data, which is the largest labeled Bitcoin address behavior dataset publicly
available to our knowledge. We then use our proposed dataset on common machine
learning models, namely: k-nearest neighbors algorithm, decision tree, random
forest, multilayer perceptron, and XGBoost. The results show that the accuracy
rates of these machine learning models for the multi-classification task on our
proposed dataset are between 93.24% and 97.13%. We also analyze the proposed
features and their relationships from the experiments, and propose a k-hop
subgraph generation algorithm to extract a k-hop subgraph from the entire
Bitcoin transaction graph constructed by the directed heterogeneous multigraph
starting from a specific Bitcoin address node (e.g., a known transaction
associated with a criminal investigation). Besides, we initially analyze the
behavior patterns of different types of Bitcoin addresses according to the
extracted features.