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Linking Amplification DDoS Attacks to Booter Services

  • Johannes KruppEmail author
  • Mohammad Karami
  • Christian Rossow
  • Damon McCoy
  • Michael Backes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10453)

Abstract

We present techniques for attributing amplification DDoS attacks to the booter services that launched the attack. Our k-Nearest Neighbor (k-NN) classification algorithm is based on features that are characteristic for a DDoS service, such as the set of reflectors used by that service. This allows us to attribute DDoS attacks based on observations from honeypot amplifiers, augmented with training data from ground truth attack-to-services mappings we generated by subscribing to DDoS services and attacking ourselves in a controlled environment. Our evaluation shows that we can attribute DNS and NTP attacks observed by the honeypots with a precision of over 99% while still achieving recall of over 69% in the most challenging real-time attribution scenario. Furthermore, we develop a similarly precise technique that allows a victim to attribute an attack based on a slightly different set of features that can be extracted from a victim’s network traces. Executing our k-NN classifier over all attacks observed by the honeypots shows that 25.53% (49,297) of the DNS attacks can be attributed to 7 booter services and 13.34% (38,520) of the NTP attacks can be attributed to 15 booter services. This demonstrates the potential benefits of DDoS attribution to identify harmful DDoS services and victims of these services.

Notes

Acknowledgements

This work was supported in part by the German Federal Ministry of Education and Research (BMBF) through funding for the Center for IT-Security, Privacy and Accountability (CISPA) under grant 16KIS0656, by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 700176, by the US National Science Foundation under grant 1619620, and by a gift from Google. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.

Supplementary material

440190_1_En_19_MOESM1_ESM.txt (2 kb)
Supplementary material 1 (txt 2 KB)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Johannes Krupp
    • 1
    Email author
  • Mohammad Karami
    • 2
  • Christian Rossow
    • 1
  • Damon McCoy
    • 3
  • Michael Backes
    • 1
    • 4
  1. 1.CISPA, Saarland UniversitySaarbrückenGermany
  2. 2.Google, Inc.Mountain ViewUSA
  3. 3.New York UniversityNew YorkUSA
  4. 4.MPI-SWSSaarbrückenGermany

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