Nowadays the industrial Internet of things (IIoT) has been successfully utilized in smart manufacturing. The massive data in IIoT promotes the development of deep learning-based health monitoring for industrial equipment. Since monitoring data for mechanical fault diagnosis collected on different working conditions or equipment have domain mismatch, models trained with training data may not work in practical applications. Therefore, it is essential to study fault diagnosis methods with domain adaptation ability. In this paper, we propose an intelligent fault diagnosis method based on an improved domain adaptation method. Specifically, two feature extractors concerning feature space distance and domain mismatch are trained using maximum mean discrepancy (MMD) and domain adversarial training respectively to enhance feature representation. Since separate classifiers are trained for feature extractors, ensemble learning is further utilized to obtain final results. Experimental results indicate that the proposed method is effective and applicable in diagnosing faults with domain mismatch.

Deep Learning (DL) has gained popularity in network intrusion detection, due to its strong capability of recognizing subtle differences between normal and malicious network activities. Although a variety of methods have been designed to leverage DL models for security protection, whether these systems are vulnerable to adversarial examples is unknown. In this paper, we design a novel adversarial attack against DLbased Network Intrusion Detection Systems (NIDS) in the IoT environment, with only black-box accesses to the DL model in such NIDS. We introduce two techniques: (1) model extraction is adopted to replicate the black-box model with a small amount of training data; (2) a saliency map is then used to disclose the impact of each packet attribute on the detection results, and the most critical features. This enables us to efficiently generate adversarial examples using conventional methods. With these tehniques, we successfully compromise one state-of-the-art NIDS, Kitsune: the adversary only needs to modify less than 0.005% of bytes in the malicious packets to achieve an average 94.31% attack success rate.