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A Lightweight Deep Autoencoder Scheme for Cyberattack Detection in the Internet of Things

Maha Sabir1, Jawad Ahmad2,*, Daniyal Alghazzawi1

1 Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 80200, Saudi Arabia
2 School of Computing, Edinburgh Napier University, Edinburgh EH10 5DY, UK

* Corresponding Author: Jawad Ahmad. Email: email

Computer Systems Science and Engineering 2023, 46(1), 57-72. https://doi.org/10.32604/csse.2023.034277

Abstract

The Internet of things (IoT) is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision. Despite several advantages, the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals. A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries. To overcome the security challenges of IoT networks, this article proposes a lightweight deep autoencoder (DAE) based cyberattack detection framework. The proposed approach learns the normal and anomalous data patterns to identify the various types of network intrusions. The most significant feature of the proposed technique is its lower complexity which is attained by reducing the number of operations. To optimally train the proposed DAE, a range of hyperparameters was determined through extensive experiments that ensure higher attack detection accuracy. The efficacy of the suggested framework is evaluated via two standard and open-source datasets. The proposed DAE achieved the accuracies of 98.86%, and 98.26% for NSL-KDD, 99.32%, and 98.79% for the UNSW-NB15 dataset in binary class and multi-class scenarios. The performance of the suggested attack detection framework is also compared with several state-of-the-art intrusion detection schemes. Experimental outcomes proved the promising performance of the proposed scheme for cyberattack detection in IoT networks.

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APA Style
Sabir, M., Ahmad, J., Alghazzawi, D. (2023). A lightweight deep autoencoder scheme for cyberattack detection in the internet of things. Computer Systems Science and Engineering, 46(1), 57-72. https://doi.org/10.32604/csse.2023.034277
Vancouver Style
Sabir M, Ahmad J, Alghazzawi D. A lightweight deep autoencoder scheme for cyberattack detection in the internet of things. Comput Syst Sci Eng. 2023;46(1):57-72 https://doi.org/10.32604/csse.2023.034277
IEEE Style
M. Sabir, J. Ahmad, and D. Alghazzawi "A Lightweight Deep Autoencoder Scheme for Cyberattack Detection in the Internet of Things," Comput. Syst. Sci. Eng., vol. 46, no. 1, pp. 57-72. 2023. https://doi.org/10.32604/csse.2023.034277



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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