Abstract

Marcin Nawrocki, John Kristoff, Chris Kanich, Raphael Hiesgen, Thomas C. Schmidt, Matthias Wählisch,
SoK: A Data-driven View on Methods to Detect Reflective Amplification DDoS Attacks Using Honeypots,
In: Proc. of IEEE Euro Security & Privacy, p. 576–591, IEEE, July 2023.
[html][pdf][BibTeX][Abstract]

Abstract: 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.

Themes: Internet Measurement and Analysis , Network Security

 

This page generated by bibTOhtml on Sat 21 Dec 2024 12:05:05 AM UTC