Precise Defense Approach Against Small-Scale Backdoor Attacks in Industrial Internet of Things

Abstract

With the exceptional ability of deep learning to extract high-dimensional structures from massive datasets, its application in the industrial Internet of Things (IIoT) has become increasingly prevalent. However, the inherent security vulnerabilities of deep learning pose a significant threat to IIoT systems, particularly in the form of backdoor attacks. Current defense methods are primarily designed for image processing tasks, and due to the uniqueness of industrial environments, their effectiveness is significantly reduced because of the lack of precision when applied directly to the IIoT applications. To address these challenges, this article proposes a trigger detection method tailored for industrial environments, capable of precisely calculating the values of triggers during the detection process. Building on this, we introduce a saliency map-based trigger pruning method to further refine the triggers. Finally, utilizing these refined triggers, we perform trigger recovery to complete the backdoor defense against the IIoT model. Furthermore, by integrating these approaches, we construct a comprehensive detection-pruning-recovery defense framework against backdoor attacks in industrial settings. Experimental results across multiple industrial scenarios demonstrate that our method enhances the robustness of industrial applications against backdoor attacks, outperforming existing defense mechanisms.