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

26 February 2020
Konstantin Berlin
Ajay Lakshminarayanarao
ArXiv (abs)PDFHTML
Abstract

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 to generate effective training datasets. Here we describe a simple, resilient cloud infrastructure for generating ML training and testing datasets, that has enhanced the speed at which our team is able to research and keep in production a multitude of security ML models.

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