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

Autor: Khan N; Department of Computer Science Brains Institute, Peshawar, Pakistan., Mohmand MI; Department of Computer Science Brains Institute, Peshawar, Pakistan., Rehman SU; School of Sciences, Engineering and Environment, University of Salford, Salford, United Kingdom., Ullah Z; Department of Computer Science Brains Institute, Peshawar, Pakistan., Khan Z; Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh, Saudi Arabia., Boulila W; Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh, Saudi Arabia.; RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba, Tunisia.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Jun 21; Vol. 19 (6), pp. e0299666. Date of Electronic Publication: 2024 Jun 21 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0299666
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Khan et al. 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.)
Databáze: MEDLINE
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