55710

Автор(ы): 

Автор(ов): 

2

Параметры публикации

Тип публикации: 

Статья в журнале/сборнике

Название: 

Multistage system-based machine learning techniques for intrusion detection in WiFi network

ISBN/ISSN: 

20907141

DOI: 

10.1155/2019/4708201

Наименование источника: 

  • Journal of Computer Networks and Communications

Обозначение и номер тома: 

№3

Город: 

  • Egypt

Издательство: 

  • Hindawi Limited

Год издания: 

2019

Страницы: 

https://www.hindawi.com/journals/jcnc/2019/4708201/
Аннотация
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.

Библиографическая ссылка: 

Пащенко Ф.Ф., Тханг В.В. Multistage system-based machine learning techniques for intrusion detection in WiFi network // Journal of Computer Networks and Communications. 2019. №3. С. https://www.hindawi.com/journals/jcnc/2019/4708201/.