  Recent progress in machine learning has generated promising results in
behavioral malware detection. Behavioral modeling identifies malicious
processes via features derived by their runtime behavior. Behavioral features
hold great promise as they are intrinsically related to the functioning of each
malware, and are therefore considered difficult to evade. Indeed, while a
significant amount of results exists on evasion of static malware features,
evasion of dynamic features has seen limited work. This paper thoroughly
examines the robustness of behavioral malware detectors to evasion, focusing
particularly on anti-ransomware evasion. We choose ransomware as its behavior
tends to differ significantly from that of benign processes, making it a
low-hanging fruit for behavioral detection (and a difficult candidate for
evasion). Our analysis identifies a set of novel attacks that distribute the
overall malware workload across a small set of cooperating processes to avoid
the generation of significant behavioral features. Our most effective attack
decreases the accuracy of a state-of-the-art classifier from 98.6% to 0% using
only 18 cooperating processes. Furthermore, we show our attacks to be effective
against commercial ransomware detectors even in a black-box setting.
