COVID-19 themed Cryptocurrency Scams

Overview

As COVID-19 has been spreading across the world since early 2020, a growing number of malicious campaigns are exploiting the COVID-19 pandemic. Cryptocurrency has attracted great attentions from attackers in this pandemic, namely, COVID-19 themed cryptocurrency scams are increasingly popular during the pandemic. However, these newly emerging scams are poorly understood by our community. In this paper, we present the first measurement study of COVID-19 themed cryptocurrency scams. We first create a comprehensive taxonomy of COVID-19 scams by manually analyzing the existing scams reported by users from online resources. Then, we propose a hybrid approach to perform the investigation by: 1) collecting reported scams in the wild; and 2) detecting undisclosed ones based on information collected from suspicious entities (e.g., domains, tweets, etc). We have collected 195 confirmed COVID-19 cryptocurrency scams in total, including 91 token scams, 19 giveaway scams, 9 blackmail scams, 14 crypto malware scams, 9 Ponzi scheme scams, and 53 donation scams. We then identified over 200 blockchain addresses associated with these scams, which lead to at least 330K US dollars in losses from 6,329 victims. For each type of scams, we further investigated the tricks and social engineering techniques they used. To facilitate future research, we have released all the well-labelled scams to the community.

About the Dataset

There are 195 scam cases in the dataset in total, which correlate with 201 scam blockchain addresses, 57 scam domains, 14 crypto malware, 47 social accounts and 91 coronavirus-related tokens. You can click here to check this dataset or download the dataset.

If you use our dataset in your research, please cite our paper: https://arxiv.org/abs/2007.13639

@misc{xia2020dont,
    title={Don't Fish in Troubled Waters! Characterizing Coronavirus-themed Cryptocurrency Scams},
    author={Pengcheng Xia and Haoyu Wang and Xiapu Luo and Lei Wu and Yajin Zhou and Guangdong Bai and Guoai Xu and Gang Huang and Xuanzhe Liu},
    year={2020},
    eprint={2007.13639},
    archivePrefix={arXiv},
    primaryClass={cs.CR}
}