  Honeypots are a deceptive technology used to capture malicious activity. The
technology is useful for studying attacker behavior, tools, and techniques but
can be difficult to implement and maintain. Historically, a lack of measures of
effectiveness prevented researchers from assessing honeypot implementations.
The consequence being ineffective implementations leading to poor performance,
flawed imitation of legitimate services, and premature discovery by attackers.
Previously, we developed a taxonomy for measures of effectiveness in dynamic
honeypot implementations. The measures quantify a dynamic honeypot's
effectiveness in fingerprinting its environment, capturing valid data from
adversaries, deceiving adversaries, and intelligently monitoring itself and its
surroundings. As a step towards developing automated effectiveness testing,
this work introduces a tool for priming a target honeypot for evaluation. We
outline the design of the tool and provide results in the form of quantitative
calibration data.
