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 eval- uation 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. Fur- thermore, 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 ser- vices. This demonstrates the potential benefits of DDoS attribution to identify harmful DDoS services and victims of these services.
International Symposium on Research in Attacks, Intrusions, and Defenses