This paper presents a socio-technical study about perceptions of human trustworthiness as a key component for countering insider threats in virtual collaborative context. This study focuses on understanding how anomalous behavior can be detected by observers in a close social network. While human observations are fallible, this study adopts the concept of human-observed changes in behavior as analogous to ???sensors??? on a computer network. Using online team-based game-playing, this study seeks to re-create realistic situations in which human sensors have the opportunity to observe changes in the behavior of a focal individual ??? in this case a team leader. Four sets of experimental situations are created to test hypotheses. Results of this study may lead to the development of semi-automated or fully-automated behavioral detection systems that attempt to predict the occurrence of malfeasance.
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