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

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review


  • Marcin Nawrocki - , Free University of Berlin (Author)
  • John Kristoff - , University of Illinois at Chicago (Author)
  • Raphael Hiesgen - , Hamburg University of Applied Sciences (Author)
  • Chris Kanich - , University of Illinois at Chicago (Author)
  • Thomas C. Schmidt - , Hamburg University of Applied Sciences (Author)
  • Matthias Wählisch - , Chair of Distributed and Networked Systems (Author)


In this paper, we revisit the use of honeypots for detecting reflective amplification attacks. These measurement tools require careful design of both data collection and data analysis including cautious threshold inference. We survey common amplification honeypot platforms as well as the underlying methods to infer attack detection thresholds and to extract knowledge from the data. By systematically exploring the threshold space, we find most honeypot platforms produce comparable results despite their different configurations. Moreover, by applying data from a large-scale honeypot deployment, network telescopes, and a real-world baseline obtained from a leading DDoS mitigation provider, we question the fundamental assumption of honeypot research that convergence of observations can imply their completeness. Conclusively we derive guidance on precise, reproducible honeypot research, and present open challenges.


Original languageEnglish
Title of host publicationProceedings - 8th IEEE European Symposium on Security and Privacy, Euro S and P 2023
Number of pages16
ISBN (electronic)9781665465120
Publication statusPublished - Jul 2023

External IDs

Scopus 85168159713
ORCID /0000-0002-3825-2807/work/142241908
Mendeley d251d94f-59ad-3790-b90c-696700c1c85e