An Evaluation of Bitcoin Address Classification based on Transaction History Summarization

Abstract

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.

Application of Gradient Boosting Algorithms for Anti-money Laundering in Cryptocurrencies

“…Others relied on online sources (flagged by communities) [15,46] or by employing heuristic-based reasoning processes (e.g., reuse of the same addresses) to categorise instances [45,49]. Some researchers focus on detecting a specific kind of criminal activity such as Bitcoin accounts linked to High Yielding Investment Programs (HYIP) [46] or Ponzi Schemes [3], while others link cryptocurrency accounts or transactions to multiple labels (multiclass classification), e.g., mixer services, dark marketplaces, exchanges, wallet providers, scams, gambling, terrorists, and ransomware [19,29,30,45,47,58]. Others focus on a binary classification, grouping illegal/illicit vs legal/licit activities [15,26,33,49].…”

“…Previous research primarily focuses on detecting illicit activities on one of the following approaches: at an account level [15,30,45], or at a transaction level [26,49]. When these ensembles were compared head-to-head against each other [30,45,46] at an accountlevel, some reported that RF outperforms tree-based gradient boosting [46], while others suggested otherwise [30,45]. However, we could not identify a comparative analysis investigating the effectiveness of these two ensembles on both approaches.…”

“…First, although it is evident from the literature that tree-based ensembles are one of the most widely used models in this domain, in particular, RF [2,3,26,30,46,49,58] and tree-based gradient boosting [15,19,30,45,46,58], it is still unclear which ensemble is most effective in detecting illicit activities on cryptocurrency networks. Previous research primarily focuses on detecting illicit activities on one of the following approaches: at an account level [15,30,45], or at a transaction level [26,49]. When these ensembles were compared head-to-head against each other [30,45,46] at an accountlevel, some reported that RF outperforms tree-based gradient boosting [46], while others suggested otherwise [30,45].…”

Revealing the Character of Nodes in a Blockchain With Supervised Learning

“…This study can be perceived as belonging to the family of studies that investigate the possibilities of the deanonymization of nodes in cryptocurrency networks. This family tackles the problem at different levels, e.g., by identifying the IP addresses of nodes [19], or by studying transaction motifs analysis [18] or transaction history summarization [24]. This direction was summarized in [1].…”

Cryptocurrencies and Artificial Intelligence: Challenges and Opportunities

“…They used synthetic minority over-sampling technique (SMOTE) to oversample the two minority classes hosted-wallet and mixing to overcome the class imbalance problem. Modeling the problem as a classification problem as well, Lin et al [64] proposed a different set of features including moments of transaction time in higher order to train different supervised machine learning models (LR, SVM, RF, AdaBoost, LightGBM, ANN, etc.) on a labeled category data set.…”