针对信息物理系统远程状态估计的隐蔽虚假数据注入攻击

金增旺; 刘茵; 刁靖东; 王震; 孙长银; 刘志强

doi:10.16383/j.aas.c240527

针对信息物理系统远程状态估计的隐蔽虚假数据注入攻击

doi: 10.16383/j.aas.c240527 cstr: 32138.14.j.aas.c240527

金增旺^{1, 2, 3,},
刘茵^1,,
刁靖东^5,,
王震^1,,
孙长银^4,,
刘志强^1,

1.
西北工业大学网络安全学院西安 710072
2.
西北工业大学长三角研究院太仓 215400
3.
西北工业大学宁波研究院宁波 315103
4.
安徽大学人工智能学院合肥 230031
5.
中国空间技术研究院钱学森空间技术实验室北京 100094

基金项目: 国家重点研发计划(2022YFB3104005), 国家自然科学基金(U21B2008, U23B2039), 2022年度太仓市基础研究计划(TC2022JC17), 宁波市自然科学基金资助(021J046)资助

详细信息

作者简介:
金增旺：西北工业大学网络空间安全学院副教授. 主要研究方向为信息物理系统安全, 多智能体安全, 网络攻防下无人系统的安全估计与安全控制, 故障诊断与容错控制. E-mail: jin_zengwang@nwpu.edu.cn

刘茵：西北工业大学网络空间安全学院硕士研究生. 主要研究方向为信息物理系统安全. E-mail: liuyin828@mail.nwpu.edu.cn

刁靖东：中国空间技术研究院钱学森空间技术实验室博士. 主要研究方向为空间信息融合, 多源多目标跟踪和集值系统辨识. 本文通信作者. E-mail: diaojingdong@spacechina.com

王震：西北工业大学网络空间安全学院教授. 主要研究方向为人工智能, 网络空间智能对抗, 智能无人系统基础与应用. E-mail: wzhen@nwpu.edu.cn

孙长银：安徽大学人工智能学院教授. 主要研究方向为智能控制与优化, 强化学习, 神经网络. E-mail: cysun@ahu.edu.cn

刘志强：西北工业大学网络空间安全学院教授. 主要研究方向为网络化系统, 故障诊断及应用. E-mail: zqliu@nwpu.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2024-07-26
- 录用日期: 2024-10-28
- 网络出版日期: 2024-11-18

Stealthy False Data Injection Attacks Against Remote State Estimation of Cyber-physical Systems

JIN Zeng-Wang^{1, 2, 3
,},
LIU Yin^1
,,
DIAO Jing-Dong^5
,,
WANG Zhen^1
,,
SUN Chang-Yin^4
,,
LIU Zhi-Qiang^1
,

1.
School of Cybersecurity, Northwestern Polytechnical University, Xi'an, 710072
2.
Yangtze River Delta Research Institute of NPU, Taicang, 215400
3.
Ningbo Institute of Northwestern Polytechnical University, Northwestern Polytechnical University, Ningbo, 315103
4.
School of Artificial Intelligence, Anhui University, Hefei, 230031
5.
Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing, 100094

Funds: Supported by National Key Research and Development Project (2022YFB3104005), National Natural Science Foundation (U21B2008, U23B2039), Basic Research Programs (2022) of Taicang (TC2022JC17), Ningbo Natural Science Foundation (2021J046)

More Information

Author Bio:
JIN Zeng-Wang　Associate Professor at the School of Cybersecurity, Northwestern Polytechnical University. His research interest covers cyber-physical system security, multi-agent system security, security estimation and security control of unmanned systems under cyber attack and defense, fault diagnosis and tolerant control

LIU Yin　Master student at the School of Cybersecurity, Northwestern Polytechnical University. Her main research interest is cyber-physical systems security

DIAO Jing-Dong　Ph. D. at Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology. His research interests covers spatial information fusion, multi-source multi-target tracking, and set-valued system identification. Corresponding author of this paper

WANG Zhen　Professor at the School of Cybersecurity, Northwestern Polytechnical University. His research interest covers artificial intelligence, intelligent countermeasures in cyberspace, and foundations and applications of intelligent unmanned systems

SUN Chang-Yin　Professor at the School of Artificial Intelligence, Anhui University. His research interest covers intelligent control and optimization, reinforcement learning, and neural networks

LIU Zhi-Qiang　Professor at the School of Cybersecurity, Northwestern Polytechnical University. His research interest covers networked systems, fault diagnosis and application

摘要

摘要: 从攻击者的角度探讨信息物理系统(Cyber-physical system, CPS)中隐蔽虚假数据注入攻击的最优策略. 选取K-L(Kullback-Leibler)散度作为攻击隐蔽性的评价指标, 设计攻击信号使得攻击保持隐蔽的同时能够最大程度地降低CPS远程状态估计的性能. 首先, 利用残差的统计特征计算远程状态估计误差协方差, 将问题转化为二次约束优化问题. 其次, 在攻击隐蔽性的约束下, 运用拉格朗日乘子法及半正定规划推导出最优策略. 最后, 通过仿真实验验证本文提出的方法与现有的方法相比在隐蔽性方面具有显著的优势.
- 信息物理系统 /
- 虚假数据注入攻击 /
- K-L散度 /
- 远程状态估计
Abstract: The optimal strategy for stealthy false data injection attacks in cyber-physical system (CPS) is explored from the attacker's perspective. The K-L (Kullback-Leibler) divergence is selected as the evaluation index of attack concealment, and the attack signals are designed to minimize the performance of CPS remote state estimation while keeping the attack concealed. First, the statistical characteristics of the residuals are used to calculate the error covariance of remote state estimation, which transforms the problem into a quadratically constrained optimization problem. Second, under the constraint of attack concealment, the optimal policy is derived using Lagrange multiplier method and semi-positive definite programming. Finally, simulation experiments are conducted to verify that the method proposed in this paper has significant advantages in terms of stealthiness compared with existing methods.
- Cyber-physical system (CPS) /
- false data injection attacks /
- K-L (Kullback-Leibler) divergence /
- remote state estimation

HTML全文

图 1 FDI攻击下CPS系统结构图

Fig. 1 Diagram of CPS structure under FDI attack

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图 2 稳定系统估计性能的退化情况

Fig. 2 Degradation of estimation performance of the stable system

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图 3 不稳定系统估计性能的退化情况

Fig. 3 Degradation of estimation performance of the unstable system

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图 4 稳定系统估计误差协方差迹的演化情况

Fig. 4 Evolution of trace of estimation error covariance for the stable system

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图 5 稳定系统残差统计特征的演化情况

Fig. 5 Evolution of statistical characteristics of the residuals of the stable system

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图 6 在不同阈值δ下稳定系统估计性能的下降情况

Fig. 6 Degradation of estimation performance of the stable system at different thresholds

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