  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.
