Improving Intrusion Detection using Genetic Linear Discriminant Analysis
Journal Title: International Journal of Intelligent Systems and Applications in Engineering - Year 2015, Vol 3, Issue 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).
Authors and Affiliations
Azween Abdullah *| School of Computing and IT, Taylors University, Subang Jaya, Selangor, Malaysia, Cai Long Zheng| Unitar International University, Petaling Jaya, Selangor, Malaysia
Keywords
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