Khan Noor Wali, Alshehri Mohammed S, Khan Muazzam A, Almakdi Sultan, Moradpoor Naghmeh, Alazeb Abdulwahab, Ullah Safi, Naz Naila, Ahmad Jawad
Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan.
Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia.
Math Biosci Eng. 2023 Jun 13;20(8):13491-13520. doi: 10.3934/mbe.2023602.
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.
物联网（IoT）是一项快速发展的技术，具有广泛的潜在应用，但物联网网络的安全性仍然是一个主要问题。现有系统在检测物联网网络中的入侵方面需要改进。一些研究人员专注于入侵检测系统（IDS），这些系统仅处理三层物联网架构中的一层，这限制了它们在检测整个网络攻击方面的有效性。为了解决这些限制，本文提出了一种基于深度学习算法的物联网网络智能IDS。所提出的模型由循环神经网络和门控循环单元（RNN-GRU）组成，它可以对物理层、网络层和应用层的攻击进行分类。所提出的模型使用专门为三层物联网系统收集的ToN-IoT数据集进行训练和测试，并且与其他公开可用的数据集相比，包含新型攻击。通过诸如准确率、精确率、召回率和F1值等多个评估指标对所提出模型进行性能分析。在模型的评估过程中应用了两种优化技术，即Adam和Adamax，发现Adam的性能是最优的。此外，将所提出的模型与各种先进的深度学习（DL）和传统机器学习（ML）技术进行了比较。结果表明，所提出的系统在网络流数据集上的准确率达到99%，在应用层数据集上的准确率达到98%，证明了其优于以前的IDS模型。
