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Multi-Dimensional Network Anomaly Detection with Machine Learning

January 2018 Presentation

In this presentation, the authors introduce the state of the art in machine learning anomaly detection and give insight into techniques to limit the errors of statistical approaches.

Abstract

In this presentation, the authors describe how recent multi-dimensional anomaly detection algorithms from machine learning can be used to combine traffic from multiple sources, while addressing the curse of dimensionality. Then, using an open-source platform of YAF, Apache Spark, and Apache Spot (incubating), they show how these algorithms can be used to provide effective focus for analysts and improve network outcomes.