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Illicit supply networks (ISNs) are composed of coordinated human actors that source, transit, and distribute illicitly traded goods to consumers, while also creating widespread social and environmental harms. Despite growing documentation of ISNs and their impacts, efforts to understand and disrupt ISNs remain insufficient due to the persistent lack of knowledge connecting a given ISN’s modus operandi and its patterns of activity in space and time. The core challenge is that the data and knowledge needed to integrate it remain fragmented and/or compartmentalized across disciplines, research groups, and agencies tasked with understanding or monitoring one or a few specific ISNs. One path forward is to conduct comparative analyses of multiple diverse ISNs. We present and apply a conceptual framework for linking ISN modus operandi to spatial-temporal dynamics and patterns of activity. We demonstrate this through a comparative analysis of three ISNs – cocaine, illegally traded wildlife, and illegally mined sand – which range from well-established to emergent, global to domestic in geographic scope, and fully illicit to de facto legal. The proposed framework revealed consistent traits related to geographic price structure, value capture at different supply chain stages, and key differences among ISN structure and operation related to commodity characteristics and their relative illicitness. Despite the diversity of commodities and ISN attributes compared, social and environmental harms inflicted by the illicit activity consistently become more widespread with increasing law enforcement disruption. Drawing on these lessons from diverse ISNs, which varied in their histories and current sophistication, possible changes in the structure and function of nascent and/or low salience ISNs may be anticipated if future conditions or law enforcement pressure change. 

Advanced Persistent Threats (APTs) are professional, sophisticated threats that pose a serious concern to our technologically-dependent society. As these threats become more common, conventional response-driven cyberattack management needs to be substituted with anticipatory defense measures. Understanding adversarial behavior and movement is critical to improve our ability to proactively defend. This paper focuses on understanding adversarial movement and adaptation using a case study from a real-time cybersecurity exercise. Through multidisciplinary methodologies from social and hard sciences, this paper presents a mechanism to dissect cyberadversarial intrusion chains to unpack movement, and adaptations.