Exchange Pattern Mining in the Bitcoin Transaction Directed Hypergraph

Ranshous, Stephen; Joslyn, Cliff; Kreyling, Sean J.; Nowak, Kathleen; Samatova, Nagiza F.; West, Curtis L.; Winters, Samuel T.

doi:10.1007/978-3-319-70278-0_16

Cited by 80 publications

(65 citation statements)

References 12 publications

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“…In this paper, we present a novel approach of how to attack Bitcoin anonymity through entity characterization. Specifically, we demonstrate how a cascading machine learning model combined with an adequate set of input features directly derived from Bitcoin blockchain data (entity and address data) as well as derived via 1 motif and 2 motif concepts introduced by Ranshous et al [28] can lead to impressive classification performance for a number of relevant Bitcoin entity classes. In fact, we were able to obtain an average global accuracy score of 99.68% with low standard deviation of 0.63% and a Matthews Correlation Coefficient (MCC) of 0.99 over 5-fold cross validation for a Gradient Boosting model using our cascading approach.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, an interesting approach is given in [28], where the concept of motifs is introduced to blockchain analysis. Authors performed an analysis of the transaction directed hypergraph in order to identify several distinct statistical properties of exchange addresses.…”

Section: Related Workmentioning

confidence: 99%

“…Results from cross-validation were considered as our baseline classification. The simple classifier was implemented in three versions applying Adaboost, Random Forest and Gradient Boosting models as those previously yielded good classification results for Bitcoin data [28]. In the second experiment, prior to entity classification according to the six classes (Table I), we built three separate classifiers, based on the additionally available address, 1 motif, and 2 motif dataframes and their respective features (7 + 9 + 18 = 34 features).…”

Section: A Classifier Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Cascading Machine Learning to Attack Bitcoin Anonymity

Zola

Eguimendia

Bruse

et al. 2019

2019 IEEE International Conference on Blockchain (Blockchain)

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Bitcoin is a decentralized, pseudonymous cryptocurrency that is one of the most used digital assets to date. Its unregulated nature and inherent anonymity of users have led to a dramatic increase in its use for illicit activities. This calls for the development of novel methods capable of characterizing different entities in the Bitcoin network.In this paper, a method to attack Bitcoin anonymity is presented, leveraging a novel cascading machine learning approach that requires only a few features directly extracted from Bitcoin blockchain data. Cascading, used to enrich entities information with data from previous classifications, led to considerably improved multi-class classification performance with excellent values of Precision close to 1.0 for each considered class. Final models were implemented and compared using different machine learning models and showed significantly higher accuracy compared to their baseline implementation.Our approach can contribute to the development of effective tools for Bitcoin entity characterization, which may assist in uncovering illegal activities.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A Classifier Modelsmentioning

confidence: 99%

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Cascading Machine Learning to Attack Bitcoin Anonymity

Zola

Eguimendia

Bruse

et al. 2019

2019 IEEE International Conference on Blockchain (Blockchain)

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“…To reduce anonymity of Bitcoin by predicting yet-unidentified addresses, [19] trained classifiers with synthetic minority over-sampling technique [20] on imbalanced data. [21] introduces the idea of motifs in directed hypergraphs, defining exchange patterns of addresses. In [6], the graphbased features motifs are then combined with address features, entity features, temporal features, and centrality features to identify Bitcoin entity categories.…”

Section: Related Workmentioning

confidence: 99%

An Evaluation of Bitcoin Address Classification based on Transaction History Summarization

Lin

Hsu

et al. 2019

2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

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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.

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“…Several studies have applied discriminative models to the problem of de-anonymizing Bitcoin transactions, with for instance the use of transaction-specific features in [32], able to achieve 70% accuracy for classifying entities into several types. In [30], the authors introduce transactions paths with application to the detection of Bitcoin exchanges, and achieve greater than 80% accuracy. Similar transactions paths features are used in [14] for a 5-class classification problem with above 90% accuracy results.…”

Section: Related Workmentioning

confidence: 99%

A Probabilistic Model of the Bitcoin Blockchain

Jourdan

Blandin

Wynter

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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The Bitcoin transaction graph is a public data structure organized as transactions between addresses, each associated with a logical entity. In this work, we introduce a complete probabilistic model of the Bitcoin Blockchain. We first formulate a set of conditional dependencies induced by the Bitcoin protocol at the block level and derive a corresponding fully observed graphical model of a Bitcoin block. We then extend the model to include hidden entity attributes such as the functional category of the associated logical agent and derive asymptotic bounds on the privacy properties implied by this model. At the network level, we show evidence of complex transaction-to-transaction behavior and present a relevant discriminative model of the agent categories. Performance of both the block-based graphical model and the network-level discriminative model is evaluated on a subset of the public Bitcoin Blockchain.

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Exchange Pattern Mining in the Bitcoin Transaction Directed Hypergraph

Cited by 80 publications

References 12 publications

Cascading Machine Learning to Attack Bitcoin Anonymity

Cascading Machine Learning to Attack Bitcoin Anonymity

An Evaluation of Bitcoin Address Classification based on Transaction History Summarization

A Probabilistic Model of the Bitcoin Blockchain

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