An Evaluation of Bitcoin Address Classification based on Transaction History Summarization

• Main Authors: Yu-Jing Lin, 林裕景
• Other Authors: Shih-wei Liao
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/tq6ctb

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 107 === Bitcoin is a cryptocurrency that features a distributed and decentralized 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. We found several useful conventional features, which we name as extra statistics. Also, we introduce new features includ- ing various high orders of moments of transaction time (represented by block height) and deciles of transaction time which summarize temporal informa- tion of the transaction history in an efficient way. The extracted features are trained by supervised machine learning methods on a labelled dataset of Bit- coin addresses. 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% / 87% with a gradient boosting decision tree algorithm.