Cryptocurrency scams: Analysis and perspectives

Abstract

Since the inception of Bitcoin in 2009, the market of cryptocurrencies has grown beyond the initial expectations, as witnessed by the thousands of tokenised assets available on the market, whose daily trades exceed dozens of USD billions. The pseudonymity features of cryptocurrencies have attracted the attention of cybercriminals, who exploit them to carry out potentially untraceable scams. The wide range of cryptocurrency-based scams observed over the last ten years has fostered the study on their effects, and the development of techniques to counter them. The research in this field is hampered by various factors. First, there exist only a few public data sources about cryptocurrency scams, and they often contain incomplete or misclassified data. Further, there is no standard taxonomy of scams, which leads to ambiguous and incoherent interpretations of their nature. Indeed, the unavailability of reliable datasets makes it difficult to train effective automatic classifiers that can detect and analyse scams. In this paper, we perform an extensive review of the scientific literature on cryptocurrency scams, which we systematise according to a novel taxonomy. By collecting and homogenising data from different public sources, we build a uniform dataset of thousands of cryptocurrency scams. We build upon this dataset to implement a tool that automatically recognises scams and classifies them according to our taxonomy. We assess the effectiveness of our tool through standard performance metrics. We then analyse the results of the classification, providing key insights about the distribution of scam types, and the correlation between different types. Finally, we propose a set of guidelines that policymakers could follow to improve user protection against cryptocurrency scams.