A Practical Approach for Taking Down Avalanche Botnets Under Real-World Constraints

Abstract

In 2016, law enforcement dismantled the infrastructure of the Avalanche bulletproof hosting service, the largest takedown of a cybercrime operation so far. The malware families supported by Avalanche use Domain Generation Algorithms (DGAs) to generate random domain names for controlling their botnets. The takedown proactively targets these presumably malicious domains; however, as coincidental collisions with legitimate domains are possible, investigators must first classify domains to prevent undesirable harm to website owners and botnet victims.

The constraints of this real-world takedown (proactive decisions without access to malware activity, no bulk patterns and no active connections) mean that approaches from the state of the art cannot be applied. The problem of classifying thousands of registered DGA domain names therefore required an extensive, painstaking manual effort by law enforcement investigators. To significantly reduce this effort without compromising correctness, we develop a model that automates the classification. Through a synergetic approach, we achieve an accuracy of 97.6% with ground truth from the 2017 and 2018 Avalanche takedowns; for the 2019 takedown, this translates into a reduction of 76.9% in manual investigation effort. Furthermore, we interpret the model to provide investigators with insights into how benign and malicious domains differ in behavior, which features and data sources are most important, and how the model can be applied according to the practical requirements of a real-world takedown.