TY - GEN
T1 - Don't Fish in Troubled Waters! Characterizing Coronavirus-Themed Cryptocurrency Scams
AU - Xia, Pengcheng
AU - Wang, Haoyu
AU - Luo, Xiapu
AU - Wu, Lei
AU - Zhou, Yajin
AU - Bai, Guangdong
AU - Xu, Guoai
AU - Huang, Gang
AU - Liu, Xuanzhe
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - As COVID-19 has been spreading across the world since early 2020, a growing number of malicious campaigns are capitalizing the topic of COVID-19. 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 research community.
AB - As COVID-19 has been spreading across the world since early 2020, a growing number of malicious campaigns are capitalizing the topic of COVID-19. 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 research community.
UR - https://www.scopus.com/pages/publications/85111971964
U2 - 10.1109/eCrime51433.2020.9493255
DO - 10.1109/eCrime51433.2020.9493255
M3 - 会议稿件
AN - SCOPUS:85111971964
T3 - eCrime Researchers Summit, eCrime
BT - Proceedings of the 2020 APWG Symposium on Electronic Crime Research, eCrime 2020
PB - IEEE Computer Society
T2 - 2020 APWG Symposium on Electronic Crime Research, eCrime 2020
Y2 - 16 November 2020 through 19 November 2020
ER -