  Despite the use of machine learning for many network traffic analysis tasks
in security, from application identification to intrusion detection, the
aspects of the machine learning pipeline that ultimately determine the
performance of the model -- feature selection and representation, model
selection, and parameter tuning -- remain manual and painstaking. This paper
presents a method to automate many aspects of traffic analysis, making it
easier to apply machine learning techniques to a wider variety of traffic
analysis tasks. We introduce nPrint, a tool that generates a unified packet
representation that is amenable for representation learning and model training.
We integrate nPrint with automated machine learning (AutoML), resulting in
nPrintML, a public system that largely eliminates feature extraction and model
tuning for a wide variety of traffic analysis tasks. We have evaluated nPrintML
on eight separate traffic analysis tasks and released nPrint and nPrintML to
enable future work to extend these methods.
