After this article [1] was published, concerns were raised about the following:
- Compliance with the PLOS Authorship policy;
- [1] appears to report a similar study and methodology as an article published later by the same author group [2],
- The article’s peer review.
In response to editorial follow-up, first author SHM stated that, though [1] and [2] share elements, the reported studies differ in analytical focus, architecture, methodological intervention, and results.
In light of the cumulative issues and concerns which were not resolved in our discussions with the authors, the PLOS One Editors issue this Expression of Concern. Readers are advised to interpret the article [1] with caution.
References
- 1. Mohammed SH, Singh MSJ, Al-Jumaily A, Islam MT, Islam MS, Alenezi AM, et al. Dual-hybrid intrusion detection system to detect False Data Injection in smart grids. PLoS One. 2025;20(1):e0316536. pmid:39869576
- 2. Mohammed SH, Jit Singh MS, Al-Jumaily A, Tariqul Islam M, Islam MdS, Alenezi AM, et al. IG-APSO-DNN: Deep learning intrusion detection model to detect false data injection attacks in smart grids. Ad Hoc Networks. 2026;180:104053.
Citation: The PLOS One Editors (2026) Expression of Concern: Dual-hybrid intrusion detection system to detect False Data Injection in smart grids. PLoS One 21(3): e0344254. https://doi.org/10.1371/journal.pone.0344254
Published: March 5, 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.