Comparison of Genetic Algorithm Optimization on Artificial Neural Network and Support Vector Machine in Intrusion Detection System

被引：0

作者：

Dastanpour, Amin ^{[1
]}

Ibrahim, Suhaimi ^{[1
]}

Mashinchi, Reza ^{[2
]}

Selamat, Ali ^{[3
]}

机构：

[1] Univ Teknol Malaysia, Adv Informat Sch, Kuala Lumpur, Malaysia

[2] Univ Technol Malaysia, Fac Comp, Johor Baharu, Malaysia

[3] Univ Technol Malaysia, Fac Comp Sci & IS, Johor Baharu, Malaysia

来源：

2014 IEEE CONFERENCE ON OPEN SYSTEMS (ICOS) | 2014年

关键词：

Genetic algorithm (GA); Artificial Neural Network (ANN); Support Vector Machine (SVM); intrusion detection; machine learning;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：72 / 77

页数：6

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