A hybrid deep learning-based intrusion detection system for IoT networks
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Published:2023
Issue:8
Volume:20
Page:13491-13520
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ISSN:1551-0018
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Container-title:Mathematical Biosciences and Engineering
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language:
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Short-container-title:MBE
Author:
Khan Noor Wali1, Alshehri Mohammed S.2, Khan Muazzam A13, Almakdi Sultan2, Moradpoor Naghmeh4, Alazeb Abdulwahab2, Ullah Safi1, Naz Naila1, Ahmad Jawad4
Affiliation:
1. Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan 2. Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia 3. ICESCO Chair Big Data Analytics and Edge Computing, Quaid-i-Azam University, Islamabad 44000, Pakistan 4. School of Computing, Engineering & The Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Abstract
<abstract><p>The Internet of Things (IoT) is a rapidly evolving technology with a wide range of potential applications, but the security of IoT networks remains a major concern. The existing system needs improvement in detecting intrusions in IoT networks. Several researchers have focused on intrusion detection systems (IDS) that address only one layer of the three-layered IoT architecture, which limits their effectiveness in detecting attacks across the entire network. To address these limitations, this paper proposes an intelligent IDS for IoT networks based on deep learning algorithms. The proposed model consists of a recurrent neural network and gated recurrent units (RNN-GRU), which can classify attacks across the physical, network, and application layers. The proposed model is trained and tested using the ToN-IoT dataset, specifically collected for a three-layered IoT system, and includes new types of attacks compared to other publicly available datasets. The performance analysis of the proposed model was carried out by a number of evaluation metrics such as accuracy, precision, recall, and F1-measure. Two optimization techniques, Adam and Adamax, were applied in the evaluation process of the model, and the Adam performance was found to be optimal. Moreover, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. The results show that the proposed system achieves an accuracy of 99% for network flow datasets and 98% for application layer datasets, demonstrating its superiority over previous IDS models.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
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