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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Vydáno v:Wireless communications and mobile computing Ročník 2021; číslo 1
Hlavní autoři: Xie, Bin, Dong, Xinyu, Wang, Changguang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Hindawi 2021
John Wiley & Sons, Inc
Témata:
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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Shrnutí: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 clustering algorithm for detecting intrusions on wireless networks based on Federated Learning. The proposed algorithm allows multiple participants to train a global model without sharing their private data and can expand the amount of data in the training model and protect the local data of each participant. Furthermore, the cosine distance of multiple perspectives is introduced in the algorithm to measure the similarity between network data objects in the improved k-means clustering process, making the clustering results more reasonable and the judgment of network data behavior more accurate. The AWID, an open wireless network attack data set, is selected as the experimental data set. Its dimensionality reduces by the method of principal component analysis (PCA). Experimental results show that the improved k-means clustering intrusion detection algorithm based on Federated Learning has better performance in detection rate, false detection rate, and detection of unknown attack types.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1530-8669
1530-8677
DOI:10.1155/2021/9322368