A First Look at the Crypto-Mining Malware Ecosystem: A Decade of Unrestricted Wealth

S. Pastrana, Guillermo Suarez-Tangil
{"title":"A First Look at the Crypto-Mining Malware Ecosystem: A Decade of Unrestricted Wealth","authors":"S. Pastrana, Guillermo Suarez-Tangil","doi":"10.1145/3355369.3355576","DOIUrl":null,"url":null,"abstract":"Illicit crypto-mining leverages resources stolen from victims to mine cryptocurrencies on behalf of criminals. While recent works have analyzed one side of this threat, i.e.: web-browser cryptojacking, only commercial reports have partially covered binary-based crypto-mining malware. In this paper, we conduct the largest measurement of crypto-mining malware to date, analyzing approximately 4.5 million malware samples (1.2 million malicious miners), over a period of twelve years from 2007 to 2019. Our analysis pipeline applies both static and dynamic analysis to extract information from the samples, such as wallet identifiers and mining pools. Together with OSINT data, this information is used to group samples into campaigns. We then analyze publicly-available payments sent to the wallets from mining-pools as a reward for mining, and estimate profits for the different campaigns. All this together is is done in a fully automated fashion, which enables us to leverage measurement-based findings of illicit crypto-mining at scale. Our profit analysis reveals campaigns with multi-million earnings, associating over 4.4% of Monero with illicit mining. We analyze the infrastructure related with the different campaigns, showing that a high proportion of this ecosystem is supported by underground economies such as Pay-Per-Install services. We also uncover novel techniques that allow criminals to run successful campaigns.","PeriodicalId":20640,"journal":{"name":"Proceedings of the Internet Measurement Conference 2018","volume":"36 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"70","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Internet Measurement Conference 2018","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3355369.3355576","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 70

Abstract

Illicit crypto-mining leverages resources stolen from victims to mine cryptocurrencies on behalf of criminals. While recent works have analyzed one side of this threat, i.e.: web-browser cryptojacking, only commercial reports have partially covered binary-based crypto-mining malware. In this paper, we conduct the largest measurement of crypto-mining malware to date, analyzing approximately 4.5 million malware samples (1.2 million malicious miners), over a period of twelve years from 2007 to 2019. Our analysis pipeline applies both static and dynamic analysis to extract information from the samples, such as wallet identifiers and mining pools. Together with OSINT data, this information is used to group samples into campaigns. We then analyze publicly-available payments sent to the wallets from mining-pools as a reward for mining, and estimate profits for the different campaigns. All this together is is done in a fully automated fashion, which enables us to leverage measurement-based findings of illicit crypto-mining at scale. Our profit analysis reveals campaigns with multi-million earnings, associating over 4.4% of Monero with illicit mining. We analyze the infrastructure related with the different campaigns, showing that a high proportion of this ecosystem is supported by underground economies such as Pay-Per-Install services. We also uncover novel techniques that allow criminals to run successful campaigns.
第一眼加密挖矿恶意软件生态系统:无限制财富的十年
非法加密挖矿利用从受害者那里窃取的资源来代表罪犯挖掘加密货币。虽然最近的工作已经分析了这种威胁的一个方面,即:web浏览器加密劫持,但只有商业报告部分涵盖了基于二进制的加密挖掘恶意软件。在本文中,我们对迄今为止最大的加密挖矿恶意软件进行了测量,在2007年至2019年的12年间分析了大约450万个恶意软件样本(120万个恶意矿工)。我们的分析管道应用静态和动态分析来从样本中提取信息,例如钱包标识符和矿池。与OSINT数据一起,这些信息用于将样本分组到活动中。然后,我们分析从矿池发送到钱包的公开可用付款作为采矿奖励,并估计不同活动的利润。所有这些都是以完全自动化的方式完成的,这使我们能够大规模利用基于测量的非法加密挖矿结果。我们的利润分析显示,有数百万美元的收入,超过4.4%的门罗币与非法采矿有关。我们分析了与不同活动相关的基础设施,表明这个生态系统的很大一部分是由地下经济(如按安装付费服务)支持的。我们还发现了允许犯罪分子成功开展活动的新技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信