Performance Analysis of an Intrusion Detection Systems Based of Artificial Neural Network

被引：6

作者：

Saber, Mohammed ^{[1
]}

El Farissi, Ilhame ^{[1
]}

Chadli, Sara ^{[2
]}

Emharraf, Mohamed ^{[1
]}

Belkasmi, Mohammed Ghaouth ^{[1
]}

机构：

[1] First Mohammed Univ, Natl Sch Appl Sci, Lab LSE2I, Oujda, Morocco

[2] First Mohammed Univ, Fac Sci, Lab Elect & Syst, Oujda, Morocco

来源：

EUROPE AND MENA COOPERATION ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGIES | 2017年 / 520卷

关键词：

Intrusion detection system; Artificial neural network for pattern recognition; KDD data; KDD parameters; Attack categories;

D O I：

10.1007/978-3-319-46568-5_52

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Artificial Neural Network (ANN) enables systems to think and act intelligently. In recent years, ANNs are applied in security of network. Therefore, there are several researches in this area, particularly in Intrusion Detection System which are based on ANN. The objective of this paper is to select the most important and crucial parameters in order to provide an optimized ANN for Pattern Recognition which is able to detect attacks including the recently developed ones. First of all, we have taken some and all of the basic attributes to aliment the networks input and to verify the dependence between these parameters and attacks. Then, we have added the parameters relating to content and time-based ones in order to demonstrate their utility and performance and also to present in which case they are crucial.

引用

下载

页码：511 / 521

页数：11

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