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Open Access

Enhanced IDS with Deep Learning for IoT-Based Smart Cities Security

Chaimae Hazman1, Azidine Guezzaz1, Said Benkirane1, Mourade Azrour2
Higher School of Technology Essaouira, Cadi Ayyad University, Marrakesh 81000, Morocco
Faculty of Sciences and Technologies, Moulay Ismail University, Errachidia 52000, Morocco
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Abstract

Cyberattacks against highly integrated Internet of Things (IoT) servers, apps, and telecoms infrastructure are rapidly increasing when issues produced by IoT networks go unnoticed for an extended period. IoT interface attacks must be evaluated in real-time for effective safety and security measures. This study implements a smart intrusion detection system (IDS) designed for IoT threats, and interoperability with IoT connectivity standards is offered by the identity solution. An IDS is a common type of network security technology that has recently received increasing interest in the research community. The system has already piqued the curiosity of scientific and industrial communities to identify intrusions. Several IDSs based on machine learning (ML) and deep learning (DL) have been proposed. This study introduces IDS-SIoDL, a novel IDS for IoT-based smart cities that integrates long shortterm memory (LSTM) and feature engineering. This model is tested using tensor processing unit (TPU) on the enhanced BoT-IoT, Edge-IIoT, and NSL-KDD datasets. Compared with current IDSs, the obtained results provide good assessment features, such as accuracy, recall, and precision, with approximately 0.9990 recording time and calculating times of approximately 600 and 6 ms for training and classification, respectively.

Keywords

IoT securityintrusion detectionMLDLLSTMTPU

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Tsinghua Science and Technology
Volume 29 Issue 4,
August 2024
Pages 929-947
DOI: 10.26599/TST.2023.9010033
Cite this article:
Hazman C, Guezzaz A, Benkirane S, et al. Enhanced IDS with Deep Learning for IoT-Based Smart Cities Security. Tsinghua Science and Technology, 2024, 29(4): 929-947. https://doi.org/10.26599/TST.2023.9010033

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Received: 20 February 2023
Revised: 26 March 2023
Accepted: 14 April 2023
Published: 09 February 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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