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Real-world data collection poses an important challenge in the security field. Insider and masquerader attack data collection poses even a greater challenge. Very few organizations acknowledge such breaches because of liability concerns and potential implications on their market value. This caused the scarcity of real-world data sets that could be used to study insider and masquerader attacks. Moreover, user studies conducted to collect such data lack rigor in their design and execution. In this paper, the authors present the methodology followed to conduct a user study and build a data set for evaluating masquerade attack detection techniques.
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