Threat hunting is an operational security process where an expert analyzes traffic, applying knowledge and lightweight tools on unlabeled data in order to identify and classify previously unknown phenomena. In this paper, we examine threat hunting metrics and practice by studying the detection of Crackonosh, a cryptojacking malware package, has on various metrics for identifying its behavior. Using a metric for discoverability, we model the ability of defenders to measure Crackonosh traffic as the malware population decreases, evaluate the strength of various detection methods, and demonstrate how different darkspace sizes affect both the ability to track the malware, but enable emergent behaviors by exploiting attacker mistakes.