Precise Defense Approach Against Small-Scale Backdoor Attacks in Industrial Internet of Things
发布时间：2024-11-17 点击次数：
姚羽（教授）
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- 博士生导师 硕士生导师
- 电子邮箱：
- 职务：复杂网络系统安全保障技术教育部工程研究中心主任
- 学历：博士研究生毕业
- 性别：男
- 联系方式：yaoyu@mail.neu.edu.cn
- 学位：博士
- 毕业院校：东北大学
- 所属院系：计算机科学与工程学院
- 学科：
计算机应用技术
计算机软件与理论
计算机系统结构
- 发表刊物：IEEE Internet of Things Journal
- 影响因子：8.2
- 摘要：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 paper 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.
- 关键字：Backdoor attack, backdoor defense, Industrial Internet of Things (IIoT)
- 论文类型：SCI JCR Q1
- 备注：https://ieeexplore.ieee.org/abstract/document/10753426
- 学科门类：工学
- 文献类型：JCR 一区
- 一级学科：计算机科学与技术
- 是否译文：否