Improving Intrusion Detection using Genetic Linear Discriminant Analysis

Year 2015, Volume: 3 Issue: 1, 34 - 39, 13.01.2015

Azween Abdullah , Long Zheng Cai

https://doi.org/10.18201/ijisae.37262

Cited By: 1

Abstract

The objective of this research is to propose an efficient soft computing approach with high detection rates and low false alarms while maintaining low cost and shorter detection time for intrusion detection. Our results were promising as they showed the new proposed system, hybrid feature selection approach of Linear Discriminant Analysis and Genetic Algorithm (GA) called Genetic Linear Discriminant Analysis (GLDA) and Support Vector Machines (SVM) Kernels as classifiers with different combinations of NSL-KDD data sets is an improved and effective solution for intrusion detection system (IDS).

Keywords

IDS; Features selection; Features transformation; NSL-KDD; GLDA; SVM Kernels

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