Adversarial Security Verification of Data-Driven FDC Systems
Yue Zhuo, Zhiqiang Ge · IEEE Transactions on Reliability · 2022
Data-driven fault detection and classification (FDC) systems play an important role in ensuring the stability and security of modern industry. However, the security issue of the data-driven FDC itself poses new challenges, where the model prediction can be seriously damaged by maliciously manipulated imperceptible perturbations, known as the adversarial attack. Since the adversarial attacks may threaten the FDC models and even the whole safety-critical industrial systems, there is an urgent need for the guarantee of data-driven model security. In this article, a scheme is presented for formally and completely verifying the security properties by treating the target problem as a convex mathematical programming. The major contribution on methodology is the verification under multiple norms via multiobjective optimization with a novel Pareto front approximation algorithm. Moreover, this work studies security verification for both supervised (fault classification) and unsupervised (fault detection) models under all mainstream norms simultaneously. For four basic data-driven models on two industrial datasets, our exclusive verification scheme provides deep and novel security insight into FDC systems. Moreover, we compare with related works to validate the algorithm performances of verification and Pareto front approximation.