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Correction

Correction: Saeed et al. Anomaly Detection in 6G Networks Using Machine Learning Methods. Electronics 2023, 12, 3300

by
Mamoon M. Saeed
1,
Rashid A. Saeed
2,
Maha Abdelhaq
3,*,
Raed Alsaqour
4,*,
Mohammad Kamrul Hasan
5 and
Rania A. Mokhtar
2
1
Department of Communications and Electronics Engineering, Faculty of Engineering, University of Modern Sciences (UMS), Baghdad Street, Sanaa 20031, Yemen
2
Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
3
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
4
Department of Information Technology, College of Computing and Informatics, Saudi Electronic University, P.O. Box 93499, Riyadh 11673, Saudi Arabia
5
Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
*
Authors to whom correspondence should be addressed.
Electronics 2025, 14(15), 2930; https://doi.org/10.3390/electronics14152930
Submission received: 2 July 2025 / Accepted: 14 July 2025 / Published: 23 July 2025
Versions Notes
In the original publication [1], the citation referring to references 10, 49, and 52 in the manuscript has been retracted. Concerns were raised on References 22, 27, and 83.
Due to this matter, the following references were removed from the reference list:
10. Saeed, M.M.; Saeed, R.A.; Mokhtar, R.A.; Alhumyani, H.; Ali, E.S. A Novel Variable Pseudonym Scheme for Preserving Privacy User Location in 5G Networks. Secur. Commun. Netw. 2022, 7487600.
22. Panchiwala, S.; Shah, M. Information, and Management. A comprehensive study on critical security issues and challenges of the IoT world. J. Data Inf. Manag. 2020, 2, 257–278.
27. Fatima, Z.; Tanveer, M.H.; Waseemullah; Zardari, S.; Naz, L.F.; Khadim, H.; Ahmed, N.; Tahir, M. Production Plant and Warehouse Automation with IoT and Industry 5.0. Appl. Sci. 2022, 12, 2053.
49. Alatabani, L.E.; Ali, E.S.; Mokhtar, R.A.; Saeed, R.A.; Alhumyani, H.; Hasan, M.K. Deep and Reinforcement Learning Technologies on Internet of Vehicle (IoV) Applications: Current Issues and Future Trends. J. Adv. Transp. 2022, 2022, 1947886.
52. Khalifa, O.O.; Wajdi, M.H.; Saeed, R.A.; Hashim, A.H.A.; Ahmed, M.Z.; Ali, E.S. Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm. J. Adv. Transp. 2022, 2022, 9189600.
83. Elfatih, N.M.; Hasan, M.K.; Kamal, Z.; Gupta, D.; Saeed, R.A.; Ali, E.S.; Hosain, S. Internet of vehicle’s resource management in 5G networks using AI technologies: Current status and trends. IET Commun. 2021, 16, 400–420.
Due to the removal of references 10, 22, 27, 49, 52, 83, subsequent references and the corresponding citations in the main text have been adjusted to align with the new numerical order. The following content has been updated:
In Section 2, “The authors in [49] used KNN certainty factor voting classifiers, linear discriminant analysis, and ML-based two-class classification models to minimize dimensionality. The network imbalance of the anomaly datasets was addressed using the SMOTE approach. The model was trained using two newly constructed training datasets. When 16 characteristics were selected, the experimental NSL-KDD evaluation revealed an accuracy of 83.24%, a FAR of 4.83%, a TPR of 82%, and a FPR of 5.43.” has been deleted.
In Section 2, “The authors in [52] recommended the FS approach based on genetic algorithms and logistic regression for network-based intrusion detection systems (NIDS) based on EL algorithms. The findings for CIC_IDS2017, NSL_KDD, and UNSW_NB2015, utilizing 11, 8, and 13 features, respectively, demonstrated 98.99%, 98.73%, and 97.997% accuracy with 98.75%, 96.64%, and 98.93% detection rates.” has been deleted.
The authors state that the scientific conclusions are unaffected. These corrections have been approved by the Academic Editor. The original publication has also been updated.

Reference

  1. Saeed, M.M.; Saeed, R.A.; Abdelhaq, M.; Alsaqour, R.; Hasan, M.K.; Mokhtar, R.A. Anomaly Detection in 6G Networks Using Machine Learning Methods. Electronics 2023, 12, 3300. [Google Scholar] [CrossRef]
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MDPI and ACS Style

Saeed, M.M.; Saeed, R.A.; Abdelhaq, M.; Alsaqour, R.; Hasan, M.K.; Mokhtar, R.A. Correction: Saeed et al. Anomaly Detection in 6G Networks Using Machine Learning Methods. Electronics 2023, 12, 3300. Electronics 2025, 14, 2930. https://doi.org/10.3390/electronics14152930

AMA Style

Saeed MM, Saeed RA, Abdelhaq M, Alsaqour R, Hasan MK, Mokhtar RA. Correction: Saeed et al. Anomaly Detection in 6G Networks Using Machine Learning Methods. Electronics 2023, 12, 3300. Electronics. 2025; 14(15):2930. https://doi.org/10.3390/electronics14152930

Chicago/Turabian Style

Saeed, Mamoon M., Rashid A. Saeed, Maha Abdelhaq, Raed Alsaqour, Mohammad Kamrul Hasan, and Rania A. Mokhtar. 2025. "Correction: Saeed et al. Anomaly Detection in 6G Networks Using Machine Learning Methods. Electronics 2023, 12, 3300" Electronics 14, no. 15: 2930. https://doi.org/10.3390/electronics14152930

APA Style

Saeed, M. M., Saeed, R. A., Abdelhaq, M., Alsaqour, R., Hasan, M. K., & Mokhtar, R. A. (2025). Correction: Saeed et al. Anomaly Detection in 6G Networks Using Machine Learning Methods. Electronics 2023, 12, 3300. Electronics, 14(15), 2930. https://doi.org/10.3390/electronics14152930

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