A Study on False Information Injection Attack on Dynamic State Estimation in Multi-Sensor Systems

In this thesis, the impact of false information injection is investigated for linear dynamic systems with multiple sensors. It is assumed that the Kalman filter system is unaware of the existence of false information and the adversary is trying to maximize the negative effect of the false information on the Kalman filter\u27s estimation performance. First, a brief introduction to the Kalman filter is shown in the thesis. We mathematically characterize the false information attack under different conditions. For the adversary, many closed-form results for the optimal attack strategies that maximize the Kalman filter\u27s 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 the Kalman filter\u27s estimation errors. To be concrete, a target tracking system is used as an example in the thesis. From the adversary\u27s 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 of the mean squared error matrix. Numerical results are also provided in order to illustrate the negative effect which the proposed attack strategies could inflict on the Kalman filter