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Generating up to date, well labeled datasets for machine learning (ML)security models is a unique engineering challenge, as large data volumes,complexity of labeling, and constant concept drift makes it difficult togenerate effective training datasets. Here we describe a simple, resilientcloud infrastructure for generating ML training and testing datasets, that hasenhanced the speed at which our team is able to research and keep in productiona multitude of security ML models.

A Simple and Agile Cloud Infrastructure to Support Cybersecurity Oriented Machine Learning Workflows