Precise Defense Method Against Low-Magnitude Poisoning Attack in Industrial Internet of Things
发布时间：2026-09-21 点击次数：
姚羽（教授）
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- 博士生导师 硕士生导师
- 所在单位：计算机科学与工程学院
- 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：
- 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
- 论文名称：Precise Defense Method Against Low-Magnitude Poisoning Attack in Industrial Internet of Things
- 发表刊物：Expert Systems with Applications
- 影响因子：9.4
- 摘要：As the digital transformation of the industrial environment accelerates, the application of deep learning in the Industrial Internet of Things (IIoT) has become increasingly popular. However, deep neural networks (DNNs) face a serious threat known as “poison attack”, which is particularly prominent in industrial environments. Unlike the image domain, where noticeable perturbations are typically required to influence model behavior, industrial data exhibit high stability, enabling attackers to compromise model integrity using only small perturbations. This “low-magnitude” vulnerability challenges existing poisoning defenses, most of which are adapted from the image domain and thus fail to capture the unique properties and subtle variation patterns of industrial data, resulting in reduced effectiveness in IIoT scenarios. To address these limitations, this paper proposes a poisoning attack mitigation framework tailored for centralized industrial deep learning classification models, covering both the data uploading/storage stage and the model training stage, for which we propose a precise data sanitisation method and a model enhancement method. Experiments conducted on multiple industrial datasets across various application scenarios demonstrate the effectiveness of the proposed approach, outperforming existing defense methods.
- 关键字：Poisoning attack; Industrial internet of thing; Deep neural network; Industrial security
- 论文类型：SCI JCR Q1
- 备注：https://www.sciencedirect.com/science/article/abs/pii/S0957417426021536
- 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