Generative Adversarial Networks for Bitcoin Data Augmentation

TL;DR

This paper explores using GANs to generate synthetic Bitcoin address data to address class imbalance in entity classification, demonstrating how proper GAN configuration improves data quality for underrepresented classes.

Contribution

First study to apply GANs for synthetic Bitcoin address data augmentation to improve classification of underrepresented entity classes.

Findings

GAN parameters significantly influence synthetic data quality

Proper configuration yields high similarity between real and generated data

Synthetic data improves classification performance for minority classes

Abstract

In Bitcoin entity classification, results are strongly conditioned by the ground-truth dataset, especially when applying supervised machine learning approaches. However, these ground-truth datasets are frequently affected by significant class imbalance as generally they contain much more information regarding legal services (Exchange, Gambling), than regarding services that may be related to illicit activities (Mixer, Service). Class imbalance increases the complexity of applying machine learning techniques and reduces the quality of classification results, especially for underrepresented, but critical classes. In this paper, we propose to address this problem by using Generative Adversarial Networks (GANs) for Bitcoin data augmentation as GANs recently have shown promising results in the domain of image classification. However, there is no "one-fits-all" GAN solution that works for every scenario. In fact, setting GAN training parameters is non-trivial and heavily affects the quality of the generated synthetic data. We therefore evaluate how GAN parameters such as the optimization function, the size of the dataset and the chosen batch size affect GAN implementation for one underrepresented entity class (Mining Pool) and demonstrate how a "good" GAN configuration can be obtained that achieves high similarity between synthetically generated and real Bitcoin address data. To the best of our knowledge, this is the first study presenting GANs as a valid tool for generating synthetic address data for data augmentation in Bitcoin entity classification.