Feature Extraction and Feature Selection: Reducing Data Complexity with Apache Spark
This addresses efficiency challenges in cybersecurity analytics for network security professionals, but it is incremental as it applies existing methods to a specific domain.
The paper tackles the time-consuming and difficult management of feature extraction and selection for cybersecurity threat detection from heterogeneous network data, presenting an approach implemented in Apache Spark using pyspark.
Feature extraction and feature selection are the first tasks in pre-processing of input logs in order to detect cyber security threats and attacks while utilizing machine learning. When it comes to the analysis of heterogeneous data derived from different sources, these tasks are found to be time-consuming and difficult to be managed efficiently. In this paper, we present an approach for handling feature extraction and feature selection for security analytics of heterogeneous data derived from different network sensors. The approach is implemented in Apache Spark, using its python API, named pyspark.