The Naked Sun: Malicious Cooperation Between Benign-Looking Processes
Recent progress in machine learning has generated promising results in behavioral malware detection, which identifies malicious processes via features derived by their runtime behavior. Such features hold great promise as they are intrinsically related to the functioning of each malware, and are therefore 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 ransomware detectors to evasion. Ransomware 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 detector from 98.6% to 0% using only 18 cooperating processes. Furthermore, we show our attacks to be effective against commercial ransomware detectors.