International Journal of Network Security & Its Applications (IJNSA) - ERA, WJCI Indexed
ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)
Webpage URL: https://airccse.org/journal/ijnsa.html
Feature Extraction and Feature Selection : Reducing Data Complexity with Apache Spark
Dimitrios Sisiaridis and Olivier Markowitch, Universite Libre de Bruxelles, Belgium
Abstract
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.
Keywords
Machine learning, feature extraction, feature selection, security analytics, Apache Spark
Original Source URL: https://aircconline.com/ijnsa/V9N6/9617ijnsa04.pdf
Volume URL: https://airccse.org/journal/jnsa17_current.html
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