摘要翻译：
本文研究了具有多个传感器的线性动态系统中虚假信息注入的影响。假设系统不怀疑虚假信息的存在，并且攻击者试图最大化虚假信息对Kalman滤波器估计性能的负面影响。对不同条件下的虚假信息攻击进行了数学刻画。对于攻击者，从理论上推导出了使Kalman滤波器估计误差最大化的最优攻击策略的许多闭式结果。结果表明，通过选择最优相关系数和在传感器间进行功率优化分配，敌手可以显著增加Kalman滤波器的估计误差。具体来说，本文以一个目标跟踪系统为例。从敌手的角度出发，给出了不同场景下的最佳攻击策略，包括同时测量位置和速度的单传感器系统和同时测量位置和速度的多传感器系统。在给定注入偏置噪声总功率的条件下，从迹和行列式两个角度求解最优解。数值结果也说明了所提出的攻击策略的有效性。
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英文标题：
《A Study Of Optimal False Information Injection Attack On Dynamic State
  Estimation in Multi-Sensor Systems》
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作者：
Jingyang Lu and Ruixin Niu
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  In this paper, the impact of false information injection is investigated for linear dynamic systems with multiple sensors. It is assumed that the system is unsuspecting the existence of false information and the adversary is trying to maximize the negative effect of the false information on Kalman filter's estimation performance. The false information attack under different conditions is mathematically characterized. For the adversary, many closed-form results for the optimal attack strategies that maximize Kalman filter's estimation error are theoretically derived. It is shown that by choosing the optimal correlation coefficients among the bias noises and allocating power optimally among sensors, the adversary could significantly increase Kalman filter's estimation errors. To be concrete, a target tracking system is used as an example in the paper. From the adversary's point of view, the best attack strategies are obtained under different scenarios, including a single-sensor system with both position and velocity measurements, and a multi-sensor system with position and velocity measurements. Under a constraint on the total power of the injected bias noises, the optimal solutions are solved from two perspectives: trace and determinant. Numerical results are also provided in order to illustrate the effectiveness of the proposed attack strategies.
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PDF链接：
https://arxiv.org/pdf/1710.09962