Journal:IEEE Transactions on Industrial Informatics

Impact Factor:9.8

Summary:In the industrial Internet of Things (IIoT) environment, intelligent models are typically trained on proprietary industrial data, which endows them with substantial intellectual property and commercial value. To safeguard these assets, model owners usually restrict access to closed box interfaces. However, model extraction attacks allow adversaries to steal models without internal access, posing serious threats to IIoT security. Existing defense methods are mainly designed for general domains and fail to consider the unique characteristics of industrial applications. When directly applied to IIoT, they often result in degraded performance or limited applicability. Specifically, previous perturbation-based strategies can partially mitigate attacks but inevitably harm model performance, which is unacceptable in reliability-critical IIoT scenarios; meanwhile, retraining-based defenses are impractical since IIoT models are usually impossible to retrain. To address these challenges, this letter proposes a bypass-style defense method against model extraction attacks tailored for IIoT applications. The proposed approach leverages a deep hash model and a hash-conditional diffusion model to extract subtle features from industrial samples for efficient filtering, and achieves dynamic defense by constructing a malicious sample repository. Experimental results on three representative IIoT datasets demonstrate that our method outperforms existing approaches in terms of defense effectiveness while preserving model performance.

Key Words:In the industrial Internet of Things (IIoT) environment, intelligent models are typically trained on proprietary industrial data, which endows them with substantial intellectual property and commercial value. To safeguard these assets, model owners usually restrict access to closed box interfaces. However, model extraction attacks allow adversaries to steal models without internal access, posing serious threats to IIoT security. Existing defense methods are mainly designed for general domains and fail to consider the unique characteristics of industrial applications. When directly applied to IIoT, they often result in degraded performance or limited applicability. Specifically, previous perturbation-based strategies can partially mitigate attacks but inevitably harm model performance, which is unacceptable in reliability-critical IIoT scenarios; meanwhile, retraining-based defenses are impractical since IIoT models are usually impossible to retrain. To address these challenges, this letter proposes a bypass-style defense method against model extraction attacks tailored for IIoT applications. The proposed approach leverages a deep hash model and a hash-conditional diffusion model to extract subtle features from industrial samples for efficient filtering, and achieves dynamic defense by constructing a malicious sample repository. Experimental results on three representative IIoT datasets demonstrate that our method outperforms existing approaches in terms of defense effectiveness while preserving model performance.

Indexed by:SCI JCR Q1

Note:https://ieeexplore.ieee.org/abstract/document/11737564

Discipline:Engineering

Document Type:JCR 一区

First-Level Discipline:Computer Science and Technology

Translation or Not:No