  Online malware scanners are one of the best weapons in the arsenal of
cybersecurity companies and researchers. A fundamental part of such systems is
the sandbox that provides an instrumented and isolated environment (virtualized
or emulated) for any user to upload and run unknown artifacts and identify
potentially malicious behaviors. The provided API and the wealth of information
inthe reports produced by these services have also helped attackers test the
efficacy of numerous techniques to make malware hard to detect.The most common
technique used by malware for evading the analysis system is to monitor the
execution environment, detect the presence of any debugging artifacts, and hide
its malicious behavior if needed. This is usually achieved by looking for
signals suggesting that the execution environment does not belong to a the
native machine, such as specific memory patterns or behavioral traits of
certain CPU instructions.
  In this paper, we show how an attacker can evade detection on such online
services by incorporating a Proof-of-Work (PoW) algorithm into a malware
sample. Specifically, we leverage the asymptotic behavior of the computational
cost of PoW algorithms when they run on some classes of hardware platforms to
effectively detect a non bare-metal environment of the malware sandbox
analyzer. To prove the validity of this intuition, we design and implement the
POW-HOW framework, a tool to automatically implement sandbox detection
strategies and embed a test evasion program into an arbitrary malware sample.
Our empirical evaluation shows that the proposed evasion technique is durable,
hard to fingerprint, and reduces existing malware detection rate by a factor of
10. Moreover, we show how bare-metal environments cannot scale with actual
malware submissions rates for consumer services.
