Comparison of genetic algorithm optimization on artificial neural network and support vector machine in intrusion detection system

Author

Dastanpour, Amin ; Ibrahim, Suhaimi ; Mashinchi, Reza ; Selamat, Ali

Author_Institution

Adv. Inf. Sch., Univ. Teknol. Malaysia, Kuala lumpur, Malaysia

fYear

2014

fDate

26-28 Oct. 2014

Firstpage

72

Lastpage

77

Abstract

As the technology trend in the recent years uses the systems with network bases, it is crucial to detect them from threats. In this study, the following methods are applied for detecting the network attacks: support vector machine (SVM) classifier, artificial Neural Networks (ANN), and Genetic Algorithms (GA). The objective of this study is to compare the outcomes of GA with SVM and GA with ANN and then comparing the outcomes of GA with SVM and GA with ANN and other algorithms. Knowledge Discovery and Data Mining (KDD CPU99) data set has been used in this paper for obtaining the results.

Keywords

data mining; genetic algorithms; neural nets; security of data; support vector machines; ANN; SVM classifier; artificial neural network; data mining; genetic algorithm optimization; intrusion detection system; knowledge discovery; support vector machine; Artificial neural networks; Classification algorithms; Feature extraction; Genetic algorithms; Intrusion detection; Machine learning algorithms; Support vector machines; Artificial Neural Network (ANN); Genetic algorithm (GA); Support Vector Machine (SVM); intrusion detection; machine learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Open Systems (ICOS), 2014 IEEE Conference on

Conference_Location

Subang

Print_ISBN

978-1-4799-6366-9

Type

conf

DOI

10.1109/ICOS.2014.7042412

Filename

7042412