Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network
The aim of machine learning is to develop algorithms that can learn from data and solve specific problems in some context as human do. This paper presents some machine learning models applied to the intrusion detection system in WiFi network. Firstly, we present an incremental semisupervised cluster...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Journal of Computer Networks and Communications |
| Online Access: | http://dx.doi.org/10.1155/2019/4708201 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832565070887911424 |
|---|---|
| author | Vu Viet Thang F. F. Pashchenko |
| author_facet | Vu Viet Thang F. F. Pashchenko |
| author_sort | Vu Viet Thang |
| collection | DOAJ |
| description | The aim of machine learning is to develop algorithms that can learn from data and solve specific problems in some context as human do. This paper presents some machine learning models applied to the intrusion detection system in WiFi network. Firstly, we present an incremental semisupervised clustering based on a graph. Incremental clustering or one-pass clustering is very useful when we work with data stream or dynamic data. In fact, for traditional clustering such as K-means, Fuzzy C-Means, DBSCAN, etc., many versions of incremental clustering have been developed. However, to the best of our knowledge, there is no incremental semisupervised clustering in the literature. Secondly, by combining a K-means algorithm and a measure of local density score, we propose a fast outlier detection algorithm, named FLDS. The complexity of FLDS is On1.5 while the results obtained are comparable with the algorithm LOF. Thirdly, we introduce a multistage system-based machine learning techniques for mining the intrusion detection data applied for the 802.11 WiFi network. Finally, experiments conducted on some data sets extracted from the 802.11 networks and UCI data sets show the effectiveness of our new proposed methods. |
| format | Article |
| id | doaj-art-029b9d093577469eaf4deff0d71ed2c5 |
| institution | Kabale University |
| issn | 2090-7141 2090-715X |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Computer Networks and Communications |
| spelling | doaj-art-029b9d093577469eaf4deff0d71ed2c52025-02-03T01:09:28ZengWileyJournal of Computer Networks and Communications2090-71412090-715X2019-01-01201910.1155/2019/47082014708201Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi NetworkVu Viet Thang0F. F. Pashchenko1Moscow Institute of Physics and Technology (State University), Moscow, RussiaThe Department of Information and Communication Technologies, MIPT (State University), Moscow, RussiaThe aim of machine learning is to develop algorithms that can learn from data and solve specific problems in some context as human do. This paper presents some machine learning models applied to the intrusion detection system in WiFi network. Firstly, we present an incremental semisupervised clustering based on a graph. Incremental clustering or one-pass clustering is very useful when we work with data stream or dynamic data. In fact, for traditional clustering such as K-means, Fuzzy C-Means, DBSCAN, etc., many versions of incremental clustering have been developed. However, to the best of our knowledge, there is no incremental semisupervised clustering in the literature. Secondly, by combining a K-means algorithm and a measure of local density score, we propose a fast outlier detection algorithm, named FLDS. The complexity of FLDS is On1.5 while the results obtained are comparable with the algorithm LOF. Thirdly, we introduce a multistage system-based machine learning techniques for mining the intrusion detection data applied for the 802.11 WiFi network. Finally, experiments conducted on some data sets extracted from the 802.11 networks and UCI data sets show the effectiveness of our new proposed methods.http://dx.doi.org/10.1155/2019/4708201 |
| spellingShingle | Vu Viet Thang F. F. Pashchenko Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network Journal of Computer Networks and Communications |
| title | Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network |
| title_full | Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network |
| title_fullStr | Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network |
| title_full_unstemmed | Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network |
| title_short | Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network |
| title_sort | multistage system based machine learning techniques for intrusion detection in wifi network |
| url | http://dx.doi.org/10.1155/2019/4708201 |
| work_keys_str_mv | AT vuvietthang multistagesystembasedmachinelearningtechniquesforintrusiondetectioninwifinetwork AT ffpashchenko multistagesystembasedmachinelearningtechniquesforintrusiondetectioninwifinetwork |