Feature or Attribute Extraction for Intrusion Detection System using Gain Ratio and Principal Component Analysis (PCA)
O. Isaiah, Olutola Agbelusi, Olasehinde Olayemi · Communications on Applied Electronics · 2016
Intrusion detection systems (IDS) refer to a category of defense tools that is used to provide warnings indicating that a system is under attack or intrusion.The IDS monitors activities within a network and alerts security administrators of suspicious activities.This paper extracted significant or highly relevant features or attributes of the Knowledge Discovery and Data Mining 1999 (KDD "99) dataset, which is a standard benchmark dataset for all intrusion problems using two features extraction techniques: Gain Ratio for discrete attributes and Principal Component Analysis (PCA) for continuous attributes.C# Programming language was used for the implementation.Also, Microsoft Excel was used to depict the result of the extraction.The result shows that thirteen (13) attributes were highly relevant and significant.