Feature Selection Ranking and Subset-Based Techniques with Different Classifiers for Intrusion Detection

被引：13

作者：

Ghazy, Rania A. ^{[1
]}

El-Rabaie, El-Sayed M. ^{[2
]}

Dessouky, Moawad I. ^{[2
]}

El-Fishawy, Nawal A. ^{[3
]}

Abd El-Samie, Fathi E. ^{[2
]}

机构：

[1] Univ Sadat City, El Sadat City, Egypt

[2] Menoufia Univ, Fac Elect Engn, Dept Elect & Elect Commun Engn, Menoufia 32952, Egypt

[3] Menoufia Univ, Fac Elect Engn, Dept Comp Sci & Engn, Menoufia 32952, Egypt

来源：

WIRELESS PERSONAL COMMUNICATIONS | 2020年 / 111卷 / 01期

关键词：

Feature selection; Intrusion detection; Classifiers; Network attacks;

D O I：

10.1007/s11277-019-06864-3

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

This paper investigates the performance of different feature selection techniques such as ranking and subset-based techniques, aiming to find the optimum collection of features to detect attacks with an appropriate classifier. The results reveal that more accuracy of detection and less false alarms are obtained after eliminating the redundant features and determining the most useful set of features, which increases the intrusion detection system (IDS) performance.

引用

页码：375 / 393

页数：19

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