In early 2012, a backdoor Trojan malware named Flame was discovered in the wild. When fully deployed, Flame proved very hard for malware researchers to analyze. In December of that year, Wired magazine reported that before Flame had been unleashed, samples of the malware had been lurking, undiscovered, in repositories for at least two years. As Wired also reported, this was not an isolated event. Every day, major anti-virus companies and research organizations are inundated with new malware samples. Although estimates vary, according to an article published in the October 2013 issue of IEEE Spectrum, approximately 150,000 new malware strains are released each day. Not enough manpower exists to manually address the sheer volume of new malware samples that arrive daily in analysts’ queues. Malware analysts instead need an approach that allows them to sort out samples in a fundamental way so they can assign priority to the most malicious of binary files. This blog post describes research I am conducting with fellow researchers at the Carnegie Mellon University (CMU) Software Engineering Institute (SEI) and CMU’s Robotics Institute. This research is aimed at developing an approach to prioritizing malware samples in an analyst’s queue (allowing them to home in on the most destructive malware first) based on the file’s execution behavior.