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In this paper, we revisit the use of honeypots for detecting reflectiveamplification attacks. These measurement tools require careful design of bothdata collection and data analysis including cautious threshold inference. Wesurvey common amplification honeypot platforms as well as the underlyingmethods to infer attack detection thresholds and to extract knowledge from thedata. By systematically exploring the threshold space, we find most honeypotplatforms produce comparable results despite their different configurations.Moreover, by applying data from a large-scale honeypot deployment, networktelescopes, and a real-world baseline obtained from a leading DDoS mitigationprovider, we question the fundamental assumption of honeypot research thatconvergence of observations can imply their completeness. Conclusively wederive guidance on precise, reproducible honeypot research, and present openchallenges.

SoK: A Data-driven View on Methods to Detect Reflective Amplification DDoS Attacks Using Honeypots