Information Fingerprint for Secure Industrial Big Data Analytics

Data-driven artificial intelligence (AI) models have been widely used in industrial systems helping big data analytic due to its convenience and flexibility. However, adversarial attacks have the ability to mislead AI models to make incorrect predictions just by adding specific perturbation to actual samples. With the high integration of industrial systems and information technology, the reliability and safety of AI models in industrial systems have been seriously threatened. In the article, fault diagnosis models and soft sensing models rely on AI technology are verified to be vulnerable facing adversarial attack. To this end, the concept of information fingerprint for industrial data is introduced to distinguish actual samples from adversarial samples with small perturbation. With fault diagnosis models and soft sensing models as the background, information fingerprint exaction networks based on deep learning is developed to extract the information fingerprint for further analysis. It utilizes supervised contrastive pretraining and unsupervised training to realize parameter learning for the structure of siamese neural network and autoencoder. Finally, the effectiveness and feasibility of the proposed information fingerprint for adversarial sample detection are verified in two industrial benchmark cases.