An Evaluation of Bitcoin Address Classification based on Transaction History Summarization
This work addresses the issue of criminal activity detection in cryptocurrency networks, which is important for regulatory and security purposes, but it is incremental as it builds on existing classification methods.
The paper tackled the problem of identifying criminal Bitcoin addresses by proposing new features based on transaction history summarization, achieving a highest Micro-F1/Macro-F1 of 87%/86% with LightGBM.
Bitcoin is a cryptocurrency that features a distributed, decentralized and trustworthy mechanism, which has made Bitcoin a popular global transaction platform. The transaction efficiency among nations and the privacy benefiting from address anonymity of the Bitcoin network have attracted many activities such as payments, investments, gambling, and even money laundering in the past decade. Unfortunately, some criminal behaviors which took advantage of this platform were not identified. This has discouraged many governments to support cryptocurrency. Thus, the capability to identify criminal addresses becomes an important issue in the cryptocurrency network. In this paper, we propose new features in addition to those commonly used in the literature to build a classification model for detecting abnormality of Bitcoin network addresses. These features include various high orders of moments of transaction time (represented by block height) which summarizes the transaction history in an efficient way. The extracted features are trained by supervised machine learning methods on a labeling category data set. The experimental evaluation shows that these features have improved the performance of Bitcoin address classification significantly. We evaluate the results under eight classifiers and achieve the highest Micro-F1/Macro-F1 of 87%/86% with LightGBM.