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In Bitcoin entity classification, results are strongly conditioned by theground-truth dataset, especially when applying supervised machine learningapproaches. However, these ground-truth datasets are frequently affected bysignificant class imbalance as generally they contain much more informationregarding legal services (Exchange, Gambling), than regarding services that maybe related to illicit activities (Mixer, Service). Class imbalance increasesthe complexity of applying machine learning techniques and reduces the qualityof classification results, especially for underrepresented, but criticalclasses.  In this paper, we propose to address this problem by using GenerativeAdversarial Networks (GANs) for Bitcoin data augmentation as GANs recently haveshown promising results in the domain of image classification. However, thereis no "one-fits-all" GAN solution that works for every scenario. In fact,setting GAN training parameters is non-trivial and heavily affects the qualityof the generated synthetic data. We therefore evaluate how GAN parameters suchas the optimization function, the size of the dataset and the chosen batch sizeaffect GAN implementation for one underrepresented entity class (Mining Pool)and demonstrate how a "good" GAN configuration can be obtained that achieveshigh similarity between synthetically generated and real Bitcoin address data.To the best of our knowledge, this is the first study presenting GANs as avalid tool for generating synthetic address data for data augmentation inBitcoin entity classification.

Generative Adversarial Networks for Bitcoin Data Augmentation