Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network
Joint Authors
Thang, Vu Viet
Pashchenko, F. F.
Source
Journal of Computer Networks and Communications
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-04-28
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Thang, Vu Viet& Pashchenko, F. F.. 2019. Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network. Journal of Computer Networks and Communications،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1172298
Modern Language Association (MLA)
Thang, Vu Viet& Pashchenko, F. F.. Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network. Journal of Computer Networks and Communications No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1172298
American Medical Association (AMA)
Thang, Vu Viet& Pashchenko, F. F.. Multistage System-Based Machine Learning Techniques for Intrusion Detection in WiFi Network. Journal of Computer Networks and Communications. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1172298
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1172298