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

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.