Feature Extraction in Security Analytics: Reducing Data Complexity with Apache Spark

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 733)

Abstract

Feature extraction is the first task of pre-processing input logs in order to detect cybersecurity threats and attacks while utilizing machine learning. When it comes to the analysis of heterogeneous data derived from different sources, this task is found to be time-consuming and difficult to be managed efficiently. In this paper we present an approach for handling feature extraction for security analytics of heterogeneous data derived from different network sensors. The approach is implemented in Apache Spark, using its python API, named pyspark.

Keywords

Machine learning Feature extraction Security analytics Apache Spark 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.QualSec Group, Departement d’ InformatiqueUniversité Libre de BruxellesBrusselsBelgium

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