Retracted: Advancements in intrusion detection: A lightweight hybrid RNN-RF model

Khan, Nasrullah; Mohmand, Muhammad Ismail; Rehman, Sadaqat ur; Ullah, Zia; Khan, Zahid; Boulila, Wadii

Abstract

Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats.

• Journal Article Type: Article
• Acceptance Date: Feb 14, 2024
• Online Publication Date: Jun 21, 2024
• Deposit Date: Jun 27, 2024
• Publicly Available Date: Aug 27, 2025
• Journal: PLOS ONE
• Print ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 6
• Pages: e0299666
• DOI: https://doi.org/10.1371/journal.pone.0299666
• Additional Information: Retraction; ; The PLOS One Editors retract this article due to concerns about peer review, reliability of the reported conclusions, and compliance with PLOS policies.; ; Specific concerns include:; ; The article reports conclusions about the accuracy of select Recurrent Neural Network classifiers that are not supported by the methodology or results.; Elements within the article raised concerns about the article’s compliance with the PLOS Artificial Intelligence Tools and Technologies policy.; ; The corresponding author stated that no generative AI tools or automated text generation systems were used at any stage in the preparation of this article.; ; These concerns call into question the article’s validity and provenance. PLOS regrets that the issues were not identified prior to the article’s publication.; ; All authors did not agree with the retraction.; ; 6 Feb 2025: The PLOS One Editors (2025) Retraction: Advancements in intrusion detection: A lightweight hybrid RNN-RF model. PLOS ONE 20(2): e0319019. https://doi.org/10.1371/journal.pone.0319019