Malware Designed to Mine Bitcoins

A team of computer scientists at the University of California, San Diego, has taken an unprecedented, in-depth look at how malware operators use the computers they infect to mine Bitcoin, a virtual currency whose value is highly volatile.

Researchers examined more than 2,000 pieces of malware used by Bitcoin mining operations in 2012 and 2013. They were able to estimate how much money operators made off their operations and which countries were most affected. The computer scientists report that the revenue of 10 of the mining operations they studied reached at least 4,500 Bitcoin over two years. This may not seem like much, but Bitcoin's value increased from about $10 to about $1,000 during that time, with a peak of $1,100 in November 2013. One Bitcoin is currently worth about $618.

Bitcoin mining is particularly attractive for malware operators because of its low cost and because it requires little to no investment in any kind of infrastructure. "At the current stratospheric value of Bitcoin, miners with access to significant computational horsepower are literally printing money," said Danny Huang, a Ph.D. student in computer science and the first author on the study.

This has the potential to change the game in malware, explained Alex Snoeren, a professor of computer science at the Jacobs School of Engineering at UC San Diego, and one of the paper's co-authors. "If it ever becomes very profitable, it could reinvigorate the malware industry," he said.

The study is part of a larger effort by computer scientists at UC San Diego to better understand how malware operators make money, from sending spam to stealing personal information, such as credit card numbers. "These transactions show how society and technology shape each other," said Huang. Researchers will present their paper, "Botcoin: Monetizing Stolen Cycles," at the Network Distributed System Security conference Feb. 23 to 25 in San Diego. To track down transactions, researchers used techniques developed by their colleague and co-author, Sarah Meiklejohn, a Ph.D. student at the Jacobs School of Engineering.

The study was conducted in partnership with George Mason University, UC Berkeley and the International Computer Science Institute.

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