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Enhancing the security in IoT and IIoT networks: An intrusion detection scheme leveraging deep transfer learning

Ahmad, Basharat; Wu, Zhaoliang; Huang, Yongfeng; Rehman, Sadaqat Ur

Authors

Basharat Ahmad

Zhaoliang Wu

Yongfeng Huang



Abstract

The Internet of Things (IoT) networks, which are defined by their interconnected devices and data streams are an expanding attack surface for cyber adversaries. Industrial Internet of Things (IIoT) is a subset of IoT and has significant importance in-terms of security. Robust intrusion detection systems (IDS) are essential for protecting these critical infrastructures. Our research suggests a novel approach to the detection of anomalies in IoT and IIoT networks that leverages the capabilities of deep transfer learning. Our methodology begins with the EdgeIIoT dataset, which serves as the basis for our data analysis. We convert the data into an appropriate image format to enable Convolutional Neural Network (CNN)-based processing. The hyper-parameters of individual machine learning models are subsequently optimized using a Random Search algorithm. This optimization phase optimizes the performance of each model by modifying the hyper-parameters that are unique to the learning algorithms. The performance of each model is meticulously assessed subsequent to hyper-parameter optimization. The top-performing models are subsequently, strategically selected and combined using the ensemble technique. The IDS scheme’s overall detection accuracy and generalizability are improved by the integration of strengths from multiple models. The proposed scheme demonstrates significant effectiveness in identifying a broad spectrum of attacks, encompassing a total of 14 distinct attack types. This comprehensive detection capability contributes to a more secure and resilient IoT ecosystem. Furthermore, application of quantization to our best models reduces resource utilization significantly without compromising accuracy.

Citation

Ahmad, B., Wu, Z., Huang, Y., & Rehman, S. U. (2024). Enhancing the security in IoT and IIoT networks: An intrusion detection scheme leveraging deep transfer learning. Knowledge-Based Systems, 305, Article 112614. https://doi.org/10.1016/j.knosys.2024.112614

Journal Article Type Article
Acceptance Date Oct 6, 2024
Online Publication Date Oct 11, 2024
Publication Date Oct 11, 2024
Deposit Date Oct 23, 2024
Publicly Available Date Oct 12, 2026
Journal Knowledge-Based Systems
Print ISSN 0950-7051
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 305
Article Number 112614
DOI https://doi.org/10.1016/j.knosys.2024.112614

Files

This file is under embargo until Oct 12, 2026 due to copyright reasons.

Contact S.Rehman15@salford.ac.uk to request a copy for personal use.




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