  Domain generation algorithms (DGAs) prevent the connection between a botnet
and its master from being blocked by generating a large number of domain names.
Promising single-data-source approaches have been proposed for separating
benign from DGA-generated domains. Collaborative machine learning (ML) can be
used in order to enhance a classifier's detection rate, reduce its false
positive rate (FPR), and to improve the classifier's generalization capability
to different networks. In this paper, we complement the research area of DGA
detection by conducting a comprehensive collaborative learning study, including
a total of 13,440 evaluation runs. In two real-world scenarios we evaluate a
total of eleven different variations of collaborative learning using three
different state-of-the-art classifiers. We show that collaborative ML can lead
to a reduction in FPR by up to 51.7%. However, while collaborative ML is
beneficial for DGA detection, not all approaches and classifier types profit
equally. We round up our comprehensive study with a thorough discussion of the
privacy threats implicated by the different collaborative ML approaches.
