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Genetic Programming Feature Extraction with Different Robust Classifiers for Network Intrusion Detection

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 168 - Number 1
Year of Publication: 2017
Authors:
Khaled Badran, Alaa Rohim
10.5120/ijca2017914276

Khaled Badran and Alaa Rohim. Genetic Programming Feature Extraction with Different Robust Classifiers for Network Intrusion Detection. International Journal of Computer Applications 168(1):37-43, June 2017. BibTeX

@article{10.5120/ijca2017914276,
	author = {Khaled Badran and Alaa Rohim},
	title = {Genetic Programming Feature Extraction with Different Robust Classifiers for Network Intrusion Detection},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {168},
	number = {1},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {37-43},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume168/number1/27841-2017914276},
	doi = {10.5120/ijca2017914276},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In this paper, we compare the performance of three traditional robust classifiers (Neural Networks, Support Vector Machines, and Decision Trees) with and without utilizing multi-objective genetic programming in the feature extraction phase. This work argues that effective feature extraction can significantly enhance the performance of these classifiers. We have applied these three classifiers stand alone to real world five datasets from the UCI machine learning database and also to network intrusion “KDD-99 cup” dataset. Then, the experiments were repeated by adding the feature extraction phase. The results of the two approaches are compared and conclude that the effective method is to evolve optimal feature extractors that transform input pattern space into a decision space in which the performance of traditional robust classifiers can be enhanced.

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Keywords

Genetic Programming, Feature Extraction, Neural Network, Support Vector Machines, Decision Tress.