A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
Feature Extraction and Feature Selection : Reducing Data Complexity with Apache Spark
2017
International journal of network security and its applications
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
doi:10.5121/ijnsa.2017.9604
fatcat:gell6jwlkvbvfcqfgyoghdolvu