An Improved K-Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning

The existing wireless network intrusion detection algorithms based on supervised learning confront many challenges, such as high false detection rate, difficulty in finding unknown attack behaviors, and high cost in obtaining labeled training data sets. This paper presents an improved k-means cluste...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Wireless communications and mobile computing Jg. 2021; H. 1
Hauptverfasser:	Xie, Bin, Dong, Xinyu, Wang, Changguang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Hindawi 2021 John Wiley & Sons, Inc
Schlagworte:	Algorithms Behavior Cluster analysis Clustering Datasets Federated learning Intrusion detection systems Machine learning Principal components analysis Privacy Vector quantization Wireless networks
ISSN:	1530-8669, 1530-8677
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!