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动态水印攻击检测方法的鲁棒性研究

杜大军 张竞帆 张长达 费敏锐 YANGTai-Cheng

杜大军, 张竞帆, 张长达, 费敏锐, YANGTai-Cheng. 动态水印攻击检测方法的鲁棒性研究. 自动化学报, 2020, 46(x): 1−12 doi: 10.16383/j.aas.c200614
引用本文: 杜大军, 张竞帆, 张长达, 费敏锐, YANGTai-Cheng. 动态水印攻击检测方法的鲁棒性研究. 自动化学报, 2020, 46(x): 1−12 doi: 10.16383/j.aas.c200614
Du Da-Jun, Zhang Jing-Fan, Zhang Chang-Da, Fei Min-Rui, Yang Tai-Cheng. Robustness of dynamic-watermarking attack-detection method. Acta Automatica Sinica, 2020, 46(x): 1−12 doi: 10.16383/j.aas.c200614
Citation: Du Da-Jun, Zhang Jing-Fan, Zhang Chang-Da, Fei Min-Rui, Yang Tai-Cheng. Robustness of dynamic-watermarking attack-detection method. Acta Automatica Sinica, 2020, 46(x): 1−12 doi: 10.16383/j.aas.c200614

动态水印攻击检测方法的鲁棒性研究

doi: 10.16383/j.aas.c200614
基金项目: 国家自然科学基金(61773253, 61803252, 61633016, 61833011, 61473182), 111引智基地(D18003), 上海市科委项目(20JC1414000, 19500712300, 19510750300)资助
详细信息
    作者简介:

    杜大军:上海大学机电工程与自动化学院教授. 主要研究方向为机器视觉和网络化系统安全控制. E-mail: ddj@shu.edu.cn

    张竞帆:上海大学机电工程与自动化学院硕士研究生. 主要研究方向为网络控制系统的安全控制. 本文的通信作者. E-mail: shuzoooe@shu.edu.cn

    张长达:上海大学机电工程与自动化学院博士. 主要研究方向为网络控制系统的安全控制. E-mail: zhangweiran@shu.edu.cn

    费敏锐:上海大学机电工程与自动化学院教授. 主要研究方向为网络化控制系统及实现. E-mail: mrfei@staff.shu.edu.cn

    YANGTai-Cheng:英国Sussex大学工程系Reader. 主要研究方向为物联网和网络化控制系统恶意攻击的检测及防范. E-mail: t.c.yang@sussex.ac.uk

Robustness of Dynamic-Watermarking Attack-Detection Method

Funds: Supported by the National Natural Science Foundation of China(61773253, 61803252, 61633016, 61833011, 61473182), 111 Project(D18003) and Project of Science and Technology Commission of Shanghai Municipality(20JC1414000, 19500712300, 19510750300)
  • 摘要: 本文针对传统动态水印检测方法无法适用模型不确定系统的攻击检测问题, 首先分析模型不确定项导致的传统动态水印检测失效原因, 然后考虑模型不确定项和过程噪声的统计规律, 将其影响转化为对方差变化特性进行分析, 提出两个具有鲁棒性的攻击检测式以及检测式中关键时变方差阈值的确定方法; 其次采用系统失真信号功率定量刻画攻击信号造成系统性能损失程度, 理论证明了系统失真信号功率上界; 在此基础上考虑最坏情况下攻击能够躲过检测, 基于水印信号与其它混合信号相互独立性新增第三检测式, 同时理论证明了系统失真信号功率上界进一步受限范围, 进而提升不确定系统的安全性; 最后仿真算例验证了所提方法的有效性和可行性.
  • 图  1  基于DWM的主动检测框架

    Fig.  1  Active detection framework based on DWM

    图  2  基于式(6)的传统水印检测结果

    Fig.  2  Traditional watermark detection results based on formula (6)

    图  3  基于式(7)的传统水印检测结果

    Fig.  3  Traditional watermark detection results based on formula (7)

    图  4  基于检测式1的检测结果

    Fig.  4  Detection results based on Test 1

    图  5  基于检测式2的检测结果

    Fig.  5  Detection results based on Test 2

    图  6  基于检测式1的虚假数据注入攻击检测结果 (数值仿真)

    Fig.  6  Detection results based on Test 1 under false data injection attack (numerical simulation)

    图  7  基于检测式2的虚假数据注入攻击检测结果 (数值仿真)

    Fig.  7  Detection results based on Test 2 under false data injection attack (numerical simulation)

    图  8  基于检测式3的虚假数据注入攻击检测结果(数值仿真)

    Fig.  8  Detection results based on Test 3 under false data injection attack (numerical simulation)

    图  9  系统失真信号功率变化

    Fig.  9  Variation of system distortion signal power

    图  10  基于阈值检测的虚假数据注入攻击检测结果(实例仿真)

    Fig.  10  Detection results based on threshold detection under false data injection attack (real example simulation)

    图  12  基于检测式2的虚假数据注入攻击检测结果(实例仿真)

    Fig.  12  Detection results based on Test 2 under false data injection attack (real example simulation)

    图  11  基于检测式1的虚假数据注入攻击检测结果(实例仿真)

    Fig.  11  Detection results based on Test 1 under false data injection attack (real example simulation)

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  • 收稿日期:  2020-08-03
  • 录用日期:  2020-10-01
  • 网络出版日期:  2020-12-10

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