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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| Published in: | Wireless communications and mobile computing Vol. 2021; no. 1 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Oxford
Hindawi
2021
John Wiley & Sons, Inc |
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| ISSN: | 1530-8669, 1530-8677 |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Author | Xie, Bin Wang, Changguang Dong, Xinyu |
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| Cites_doi | 10.1007/s12652-020-01919-x 10.1016/j.comnet.2020.107391 10.1109/ACCESS.2020.3034015 10.1007/s12652-021-03077-0 10.1016/j.isatra.2020.11.016 10.1002/cpe.5242 10.1016/j.ins.2021.03.058 10.1145/3298981 10.1109/COMST.2015.2402161 10.1016/j.compind.2021.103459 10.1016/j.ins.2021.04.001 10.1016/j.ins.2021.03.039 10.1109/TPDS.2021.3056773 10.1007/s10922-021-09606-8 |
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| Copyright | Copyright © 2021 Bin Xie et al. Copyright © 2021 Bin Xie et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.1155/2021/9322368 |
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| SubjectTerms | Algorithms Behavior Cluster analysis Clustering Datasets Federated learning Intrusion detection systems Machine learning Principal components analysis Privacy Vector quantization Wireless networks |
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| Title | An Improved K-Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning |
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