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RETRACTED: A novel federated learning based lightweight sustainable IoT approach to identify abnormal traffic

Yasser Alharbi (College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia)

International Journal of Pervasive Computing and Communications

ISSN: 1742-7371

Article publication date: 10 June 2022

Issue publication date: 8 November 2024

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This article was retracted on 26 Nov 2024.

Retraction statement

The publishers of Alharbi, Y. (2022), “A novel federated learning based lightweight sustainable IoT approach to identify abnormal traffic”, International Journal of Pervasive Computing and Communications, Vol. 20 No. 4, pp. 439-449. https://doi.org/10.1108/IJPCC-03-2022-0119

An internal investigation into a series of submissions has uncovered evidence that the peer review process was compromised. As a result of these concerns, the findings of the article cannot be relied upon. This decision has been taken in accordance with Emerald’s publishing ethics and the COPE guidelines on retractions.

Despite numerous attempts to contact the authors, the journal has received no response; the response of the authors would be gratefully received.

The publishers of the journal sincerely apologize to the readers.

Abstract

Purpose

This strategy significantly reduces the computational overhead and storage overhead required when using the kernel density estimation method to calculate the abnormal evaluation value of the test sample.

Design/methodology/approach

To effectively deal with the security threats of botnets to the home and personal Internet of Things (IoT), especially for the objective problem of insufficient resources for anomaly detection in the home environment, a novel kernel density estimation-based federated learning-based lightweight Internet of Things anomaly traffic detection based on nuclear density estimation (KDE-LIATD) method. First, the KDE-LIATD method uses Gaussian kernel density estimation method to estimate every normal sample in the training set. The eigenvalue probability density function of the dimensional feature and the corresponding probability density; then, a feature selection algorithm based on kernel density estimation, obtained features that make outstanding contributions to anomaly detection, thereby reducing the feature dimension while improving the accuracy of anomaly detection; finally, the anomaly evaluation value of the test sample is calculated by the cubic spine interpolation method and anomaly detection is performed.

Findings

The simulation experiment results show that the proposed KDE-LIATD method is relatively strong in the detection of abnormal traffic for heterogeneous IoT devices.

Originality/value

With its robustness and compatibility, it can effectively detect abnormal traffic of household and personal IoT botnets.

Keywords

Acknowledgements

Conflicts of interest: The author declares that they have no conflicts of interest.

Citation

Alharbi, Y. (2024), "RETRACTED: A novel federated learning based lightweight sustainable IoT approach to identify abnormal traffic", International Journal of Pervasive Computing and Communications, Vol. 20 No. 4, pp. 439-449. https://doi.org/10.1108/IJPCC-03-2022-0119

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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