Robust Monitor for Industrial IoT Condition Prediction

doi:10.1109/JIOT.2022.3222439

CORC > 自动化研究所 > 中国科学院自动化研究所 > 舆论大数据科学与技术应用联合实验室

	Robust Monitor for Industrial IoT Condition Prediction
	Zhang, Xingwei 1,2; Tian, Hu 1,2; Zheng, Xiaolong 1,2; Zeng, Daniel Dajun 1,2
刊名	IEEE INTERNET OF THINGS JOURNAL
	2023-05-15
卷号	10 期号:10 页码:8618-8629
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)
ISSN号	2327-4662
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.
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关键词	HYDRAULIC SYSTEM ; INTERNET ; MODEL ; TIME
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
内容类型	期刊论文
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.