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...

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Vydané v:	Wireless communications and mobile computing Ročník 2021; číslo 1
Hlavní autori:	Xie, Bin, Dong, Xinyu, Wang, Changguang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Hindawi 2021 John Wiley & Sons, Inc
Predmet:	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
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