A Robust Deep Learning Enabled Trust-boundary Protection for Adversarial Industrial IoT Environment

Hassan, Mohammad Mehedi, Hassan, Md. Rafiul, Huda, Shamsul and de Albuquerque, Victor Hugo C. 2020, A Robust Deep Learning Enabled Trust-boundary Protection for Adversarial Industrial IoT Environment, IEEE Internet of Things Journal, vol. 8, no. 8, pp. 1-12, doi: 10.1109/jiot.2020.3019225.

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Title A Robust Deep Learning Enabled Trust-boundary Protection for Adversarial Industrial IoT Environment
Author(s) Hassan, Mohammad Mehedi
Hassan, Md. Rafiul
Huda, ShamsulORCID iD for Huda, Shamsul orcid.org/0000-0001-7848-0508
de Albuquerque, Victor Hugo C.
Journal name IEEE Internet of Things Journal
Volume number 8
Issue number 8
Start page 1
End page 12
Total pages 12
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2020-08
ISSN 2372-2541
Keyword(s) Training
Generative adversarial networks
Robustness
Gallium nitride
Data models
Internet of Things
Machine learning
Trust boundary protection
Industrial IoT
Adversarial attack
Deep learning
Summary In recent years, trust-boundary protection has become a challenging problem in Industrial Internet-of-Things (IloT) environments. Trust boundaries separate IIoT processes and data stores in different groups based on user access privilege. Points where dataflow intersects with the trust boundary are becoming entry points for attackers. Attackers use various model skewing and intelligent techniques to generate adversarial/noisy examples that are indistinguishable from natural data. Many of the existing machine learning (ML)-based approaches attempt to circumvent this problem. However, owing to an extremely large attack surface in the IIoT network, capturing a true distribution during training is difficult. The standard Generative Adversarial Network (GAN) commonly generates adversarial examples for training using randomly sampled noise. However, distribution of noisy inputs of GAN largely differs from actual distribution of data in IIoT networks and shows less robustness against adversarial attacks. Therefore, in this paper, we propose a downsampler-encoder-based cooperative data generator that is trained using an algorithm to ensure better capture of actual distribution of attack models for the large IIoT attack surface. The proposed downsampler-based data generator is alternatively updated and verified during training using a deep neural network discriminator to ensure robustness. This guarantees the performance of the generator against input sets with a high noise level at time of training and testing. Various experiments are conducted on a real IIoT test bed dataset. Experimental results show that the proposed approach outperforms conventional deep learning and other ML techniques in terms of robustness against adversarial/noisy examples in the IIoT environment.
Language eng
DOI 10.1109/jiot.2020.3019225
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2020, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30141225

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