  We investigate the detection of botnet command and control (C2) hosts in
massive IP traffic using machine learning methods. To this end, we use NetFlow
data -- the industry standard for monitoring of IP traffic -- and ML models
using two sets of features: conventional NetFlow variables and distributional
features based on NetFlow variables. In addition to using static summaries of
NetFlow features, we use quantiles of their IP-level distributions as input
features in predictive models to predict whether an IP belongs to known botnet
families. These models are used to develop intrusion detection systems to
predict traffic traces identified with malicious attacks. The results are
validated by matching predictions to existing denylists of published malicious
IP addresses and deep packet inspection. The usage of our proposed novel
distributional features, combined with techniques that enable modelling complex
input feature spaces result in highly accurate predictions by our trained
models.
