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Open Access

Retraction

Retraction: Enhancing IoT cybersecurity through lean-based hybrid feature selection and ensemble learning: A visual analytics approach to intrusion detection

  • The PLOS One Editors

The PLOS One Editors retract this article [1] due to concerns about compromised peer review and compliance with PLOS policy on Authorship. We regret that the issues were not addressed prior to the article’s publication.

In addition, the article cited as reference 16 in [1] was retracted before this article was published.

IZ, EO, SJ, HA, and AA did not agree with the retraction. SA, SH, and NP either did not respond directly or could not be reached.

Reference

  1. 1. Zada I, Omran E, Jan S, Alfraihi H, Alsalamah S, Alshahrani A, et al. RETRACTED: Enhancing IoT cybersecurity through lean-based hybrid feature selection and ensemble learning: A visual analytics approach to intrusion detection. PLoS One. 2025;20(7):e0328050. pmid:40690488

Citation: The PLOS One Editors (2026) Retraction: Enhancing IoT cybersecurity through lean-based hybrid feature selection and ensemble learning: A visual analytics approach to intrusion detection. PLoS One 21(4): e0346538. https://doi.org/10.1371/journal.pone.0346538

Published: April 6, 2026

Copyright: © 2026 The PLOS One Editors. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.