As Darktrace regularly detects crypto-mining attempts the moment they occur on a network, we can estimate the cash flow stream a cyber-criminal earned on a laterally moving Monero-miner infection that Darktrace identified.
How it began
Last month, a customer’s device – which we will call patient zero – became infected with a Monero-miner. After a short time, patient zero started looking for accessible SMB drives by scanning the internal network for devices on port 445. As the device had not conducted any network scanning activity in the past, Darktrace flagged the process as an unusual network scan and an anomalous SMB enumeration:
Once patient zero identified accessible IPC$, ADMIN$ or C$ SMB drives, it transferred an executable to the drive. After the file transfer, the malware used PsExec to connect to the device and execute the malicious software. As patient zero had not made any SMB drive writes and had not used PsExec in this fashion before, alerts were raised immediately:
Spread and containment
The now-infected device started mining Monero and attempted to communicate over Tor2Web with Command & Control (C2) servers:
Using Darktrace, the security team identified the infection within minutes and assessed the complete extent of the infection in less than an hour. Within three hours from initial detection, the security team had run a clean-up script on their network which stopped the spread.
We have estimated the hypothetical revenue for this particular attack. To make the mining less detectable, some of the current Monero-mining malware applies restrictions to both the number of threads that can be used and the maximum CPU usage capacity. As a result, we have estimated the figures below on a worst-case scenario basis.
We know that 300 machines were infected and that the Monero miners were running for around 4 hours.
Mining profitability is commonly measured in the amount of hashes calculated per second per CPU core or GPU. This number, known as hashes per second (H/S), can differ based on the hardware used. A common number on the lower end of the scale for H/S on a single CPU is 20 H/S for the CryptoNight algorithm used to mine Monero.
GPUs, being more efficient for the CryptoNight algorithm, can yield 2-3x the H/S rate of CPUs and beyond. Keeping with a worst-case scenario basis, we will assume all infected devices had only 2 CPU cores and no GPUs, meaning a single infected machine yielded 40 H/S. This leads us to the following calculation: 300 infected devices x 40 H/S = 12000 H/S.
A Monero-mining revenue calculation tool produced the following results: with a Monero price of $202.43 at the time of infection (disregarding electricity costs), the criminal would have earned roughly $15.85 in 24h. As the miners only ran for around 4 hours, the resulting revenue would have only been $2.64. So how is this profitable?
It’s a numbers game
Cryptocurrency-mining operations are designed to last for months, not hours. If this infection had gone undetected, the criminal would have earned $15.85 per day, or $475.62 per month. Furthermore, victims with larger networks are much less likely to notice the infection. As attacks spreading this kind of malware are often indiscriminate in nature, they will often hit thousands of organizations at the same time, giving them the capacity to generate much more than just half a dollar.
Max is a cyber security expert with over nine years’ experience in the field, specializing in network monitoring and offensive security. At Darktrace, Max works with strategic customers to help them investigate and respond to threats, as well as overseeing the cyber security analyst team in the Cambridge UK headquarters. Prior to his current role, Max led the Threat and Vulnerability Management department for Hewlett-Packard in Central Europe. In this role he worked as a white hat hacker, leading penetration tests and red team engagements. He was also part of the German Chaos Computer Club when he was still living in Germany. Max holds a MSc from the University of Duisburg-Essen and a BSc from the Cooperative State University Stuttgart in International Business Information Systems.