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

被引：0
|
作者：
Rania A. Ghazy
El-Sayed M. El-Rabaie
Moawad I. Dessouky
Nawal A. El-Fishawy
Fathi E. Abd El-Samie
机构：
[1] University of Sadat City,Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering
[2] Menoufia University,Department of Computer Science and Engineering, Faculty of Electronic Engineering
[3] Menoufia University,undefined
来源：
Wireless Personal Communications | 2020年 / 111卷
关键词：
Feature selection; Intrusion detection; Classifiers; Network attacks;
D O I：
暂无
中图分类号：
学科分类号：
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
收藏
页码：375 / 393
页数：18
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