2020/02/26 by Konstantin Berlin, Berlin, Konstantin, Ajay Lakshminarayanarao +1
Computer Science · #Network Security and Intrusion Detection #Anomaly Detection Techniques and Applications #Data Stream Mining Techniques
paper · pdf · doi:10.48550/arxiv.2002.11828
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.