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This paper presents an improved <jats:italic>k<\/jats:italic>\u2010means 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 <jats:italic>k<\/jats:italic>\u2010means 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 <jats:italic>k<\/jats:italic>\u2010means clustering intrusion detection algorithm based on Federated Learning has better performance in detection rate, false detection rate, and detection of unknown attack types.<\/jats:p>","DOI":"10.1155\/2021\/9322368","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T03:46:09Z","timestamp":1628653569000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Improved <i>K<\/i>\u2010Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated 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