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
收藏
页码：375 / 393
页数：19
相关论文
共 50 条
[31]   A HYBRID METHOD FOR INTRUSION DETECTION WITH GA-BASED FEATURE SELECTION [J].
Chen, Zh-Xian ;
Huang, Hao .
INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2011, 17 (02) :175-186
[32]   A Filter Feature Selection Algorithm Based on Mutual Information for Intrusion Detection [J].
Zhao, Fei ;
Zhao, Jiyong ;
Niu, Xinxin ;
Luo, Shoushan ;
Xin, Yang .
APPLIED SCIENCES-BASEL, 2018, 8 (09)
[33]   INTRUSION DETECTION BASED ON MACHINE LEARNING AND FEATURE SELECTION [J].
Alaoui, Souad ;
El Gonnouni, Amina ;
Lyhyaoui, Abdelouahid .
MENDEL 2011 - 17TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING, 2011, :199-206
[34]   Intrusion detection based on hybrid metaheuristic feature selection [J].
Zhang, Fengjun ;
Huang, Lisheng ;
Shi, Kai ;
Zhai, Shengjie ;
Lan, Yunhai ;
Li, Qinghua .
COMPUTER JOURNAL, 2024, 68 (01) :13-22
[35]   Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization [J].
Alkanhel, Reem ;
El-kenawy, El-Sayed M. ;
Abdelhamid, Abdelaziz A. ;
Ibrahim, Abdelhameed ;
Alohali, Manal Abdullah ;
Abotaleb, Mostafa ;
Khafaga, Doaa Sami .
CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 74 (02) :2677-2693
[36]   Feature Subset Selection Using Binary Gravitational Search Algorithm for Intrusion Detection System [J].
Behjat, Amir Rajabi ;
Mustapha, Aida ;
Nezamabadi-pour, Hossein ;
Sulaiman, Md. Nasir ;
Mustapha, Norwati .
INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2013), PT II, 2013, 7803 :377-386
[37]   A Feature Selection Based DNN for Intrusion Detection System [J].
Li, Li-Hua ;
Ahmad, Ramli ;
Tsai, Wen-Chung ;
Sharma, Alok Kumar .
PROCEEDINGS OF THE 2021 15TH INTERNATIONAL CONFERENCE ON UBIQUITOUS INFORMATION MANAGEMENT AND COMMUNICATION (IMCOM 2021), 2021,
[38]   Review on intrusion detection using feature selection with machine learning techniques [J].
Kalimuthan, C. ;
Renjit, J. Arokia .
MATERIALS TODAY-PROCEEDINGS, 2020, 33 :3794-3802
[39]   Supervised feature selection techniques in network intrusion detection: A critical review [J].
Di Mauro, M. ;
Galatro, G. ;
Fortino, G. ;
Liotta, A. .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2021, 101
[40]   Sequential classifiers for network intrusion detection based on data selection process [J].
Corrales, David Camilo ;
Corrales, Juan Carlos ;
Sanchis, Araceli ;
Ledezma, Agapito .
2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2016, :1827-1832
12345