Robust Monitor for Industrial IoT Condition Prediction
文献类型：期刊论文
| 作者 | Zhang, Xingwei1,2; Tian, Hu1,2 ; Zheng, Xiaolong1,2; Zeng, Daniel Dajun1,2
|
| 刊名 | IEEE INTERNET OF THINGS JOURNAL
 |
| 出版日期 | 2023-05-15 |
| 卷号 | 10期号:10页码:8618-8629 |
| ISSN号 | 2327-4662 |
| 关键词 | Perturbation methods Monitoring Industrial Internet of Things Training Predictive models Robustness Temperature sensors Adversarial perturbation adversarial training Industrial Internet of Things (IIoT) machine learning (ML) temporal convolutional network (TCN) |
| DOI | 10.1109/JIOT.2022.3222439 |
| 通讯作者 | Zheng, Xiaolong(xiaolong.zheng@ia.ac.cn) |
| 英文摘要 | The robustness of machine learning (ML) models has gained much attention along with their wide application on various safety-required Industrial Internet of Things (IIoT) paradigms. Researchers found that some specific attacks added on sensor measurements can maliciously disturb IIoT monitors that are designed using ML architectures. The Traditional detection methods could judge whether the measurements are attacked to prevent the failure of monitors. Unfortunately, recent works argue that the commonly used detection methods could be circumvented through adaptive attacks that could acquire the mechanism of detectors; they could not truly enhance the robustness of ML models. Instead, general robust mechanisms should be performed to authentically enhance the robustness of models against any potential attacks with specific restrictions. On the basis of the above argument, we design a robust condition monitor for predicting the fault condition of IIoT systems using the adversarial training technique called robust temporal convolutional network (RTCN). The model is designed to be formally robust to attacks with restricted magnitude. The temporal convolutional network (TCN) is employed to design the base structure of the monitor. TCN can capture temporal information from sensors to enhance the feature extraction performance of models. We also present a novel false data injection (FDI) attack-generating method that utilizes the conception of adversarial perturbations to disturb well-trained monitors. The experimental results verify the efficiency of feature extraction performance of our model from IIoT systems. Furthermore, adversarial training mechanism through a min-max manner could effectively improve the reliability of ML-based IIoT monitors against strong FDI attacks. |
| WOS关键词 | HYDRAULIC SYSTEM ; INTERNET ; MODEL ; TIME |
| 资助项目 | Ministry of Science and Technology of China[2020AAA0108401] ; Natural Science Foundation of China[72225011] ; Natural Science Foundation of China[71621002] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000982455700023 |
| 资助机构 | Ministry of Science and Technology of China ; Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53435]  |
| 专题 | 舆论大数据科学与技术应用联合实验室 |
| 通讯作者 | Zheng, Xiaolong |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Xingwei,Tian, Hu,Zheng, Xiaolong,et al. Robust Monitor for Industrial IoT Condition Prediction[J]. IEEE INTERNET OF THINGS JOURNAL,2023,10(10):8618-8629. |
| APA | Zhang, Xingwei,Tian, Hu,Zheng, Xiaolong,&Zeng, Daniel Dajun.(2023).Robust Monitor for Industrial IoT Condition Prediction.IEEE INTERNET OF THINGS JOURNAL,10(10),8618-8629. |
| MLA | Zhang, Xingwei,et al."Robust Monitor for Industrial IoT Condition Prediction".IEEE INTERNET OF THINGS JOURNAL 10.10(2023):8618-8629. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。