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

Speaker: Guillermo Suarez de Tangil Rotaeche

Date/Time: 15-Aug-2019, 16:00 UTC

Venue: Roberts 309



Illicit crypto-mining uses stolen resources to mine cryptocurrencies for free. This threat is now pervasive and growing rapidly. In this talk, I will cover how this ecosystem is evolving, how much harm it is causing, and how can it be stopped. I will talk about the findings obtained after conducting the largest measurement of binary-based criminal crypto-mining to date, which will soon be presented at The 2019 Internet Measurement Conference (IMC).


This measurement shows that criminals have illicitly mined about 4.4% of the Monero cryptocurrency (we estimate that this accounts for 58 million USD). We also observe that there is a considerably small number of actors that hold sway this crime. Furthermore, we note that there is an increasing level of support offered by criminals in underground markets, that allow other criminals to run inexpensive malware-driven mining campaigns. This explains why this threat has grown sharply in 2018.


Guillermo Suarez-Tangil is a Lecturer (Assistant Professor) of Computer Science at King's College London (KCL). His research focuses on systems security and malware analysis and detection. In particular, his area of expertise lies in the study of smart malware, ranging from the detection of advanced obfuscated malware to automated analysis of targeted malware. Before joining KCL, he has been senior research associate at University College London (UCL) where he has explored the use of program analysis to study malware. He has also been actively involved in other research directions aiming at detecting and preventing of Mass-Marketing Fraud (MMF) and security and privacy in the social web with the iDrama lab (


Prior to that, he held a post-doctoral position at Royal Holloway, University of London (RHUL) where he was part of the development team of CopperDroid, a tool to dynamically test malware that uses machine learning to model malicious behaviors. He also holds a solid expertise on building novel data learning algorithms for malware analysis. He obtained his PhD on smart malware analysis in Carlos III University of Madrid with distinction and received the Best National Student Academic Award---a competitive award given to the best Thesis in the field of Engineering between 2014-2015 with about 1% acceptance rate (about 100 Cum Laude Thesis were invited to compete for the only award).

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