Detecting the One-Shot Dummy Attack on the Power Industrial Control Processes With an Unsupervised Data-Driven Approach

被引：0

作者：

Zhenyong Zhang ^{[1
]}

Yan Qin ^{[2
]}

Jingpei Wang ^{[3
]}

Hui Li ^{[1
]}

Ruilong Deng ^{[4
,3
]}

机构：

[1] the State Key Laboratory of Public Big Data and the College of Computer Science and Technology, Guizhou University

[2] the School of Chemical and Biomedical Engineering, Nanyang Technological University

[3] the State Key Laboratory of Industrial Control Technology and the College of Control Science and Engineering, Zhejiang University

[4] IEEE

来源：

IEEE/CAA Journal of Automatica Sinica | 2023年 / 10卷 / 02期

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TM73 [电力系统的调度、管理、通信]; TP393.08 [];

学科分类号：

080802 ; 0839 ; 1402 ;

摘要：

Dear Editor,Dummy attack(DA), a deep stealthy but impactful data integrity attack on power industrial control processes, is recently recognized as hiding the corrupted measurements in normal measurements. In this letter, targeting a more practical case, we aim to detect the oneshot DA, with the purpose of revealing the DA once it is launched.Specifically, we first formulate an optimization problem to generate one-shot DAs. Then, an unsupervised data-driven approach based on a modified local outlier factor(MLOF) is proposed to detect them.

引用

页码：550 / 553

页数：4

共 38 条

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