Bitcoin Mixing Detection Using Deep Autoencoder

By Lihao Nan, Dacheng Tao. 2018

30 citations recorded by the source corpus.

Bitcoin is a decentralized transaction platform and the largest cryptocurrency system. Bitcoin represents a chain of blocks containing its entire legal transaction history, thereby providing convenience for tracking money. However, mixing services are used as an effective means to hide the identity of a transaction address by combining several transfers from different users. Detecting the original user of a Bitcoin address and the money flow is essential in some special circumstances such as anomaly detection. Recognizing Bitcoin mixing services and de-mixing user accounts have only rarely been studied. Here we demonstrate that Bitcoin transaction graphs possess community properties and that a mixing service can be regarded as a cluster outlier. Motivated by the success of graph embedding in social network analysis, we propose a feature-based method to identify mixing services, testing our method on the real Bitcoin ledger.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Omer Berat Sezer
2. M. Ugur Gudelek
3. A. Ozbayoglu

In Information Systems

1. A. Ozbayoglu
2. Omer Berat Sezer
3. M. Ugur Gudelek

Citations in the corpus

• Deep Learning for Financial Applications : A Survey (2020, methodology): “[146] proposed a method for bitcoin mixing detection that consisted of different stages: Constructing the Bitcoin transaction graph, implementing node embedding, detecting outliers through AE.”