The impact of false information injection is investigated for linear dynamic systems with multiple sensors. First, it is assumed that the system is unaware of 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 under a power constraint. The false information attack under different conditions is mathematically characterized. For the adversary, many closed-form results for the optimal attack strategies that maximize the Kalman filter's estimation error are theoretically derived. It is shown that by choosing the optimal correlation coefficients among the false information and allocating power optimally among sensors, the adversary could significantly increase the Kalman filter's estimation errors. In order to detect the false information injected by an adversary, we investigate the strategies for the Bayesian estimator to detect the false information and defend itself from such attacks. We assume that the adversary attacks the system with certain probability, and that he/she adopts the worst possible strategy that maximizes the mean squared error (M.S.E) if the attack is undetected. An optimal Bayesian detector is designed which minimizes the average system estimation error instead of minimizing the probability of detection error, as a conventional Bayesian detector typically does. The case that the adversary attacks the system continuously is also studied. In this case, sparse attack strategies in multi-sensor dynamic systems are investigated from the adversary's point of view. It is assumed that the defender can perfectly detect and remove the sensors once they are corrupted by false information injected by an adversary. The adversary's goal is to maximize the covariance matrix of the system state estimate by the end of attack period under the constraint that the adversary can only attack the system a few times over the sensor and over the time, which leads to an integer programming problem. In order to overcome the prohibitive complexity of the exhaustive search, polynomial-time algorithms, such as greedy search and dynamic programming, are proposed to find the suboptimal attack strategies. As for greedy search, it starts with an empty setơ̐ and one sensor is added at each iteration, whose elimination will lead to the maximum system estimation error. The process terminates when the cardinality of the active set reaches to the sparsity constraint. Greedy search based approaches such as sequential forward selection (SFS), sequential backward selection (SBS), and simplex improved sequential forward selection (SFS-SS) are discussed and corresponding attack strategies are provided. Dynamic programming is also used in obtaining the sub-optimal attack strategy. The validity of dynamic programming lies on a straightforward but important nature of dynamic state estimation systems: the credibility of the state estimate at current step is in accordance with that at previous step. The problem of false information attack on and the Kalman filter's defense of state estimation in dynamic multi-sensor systems is also investigated from a game theoretic perspective. The relationship between the Kalman filter and the adversary can be regarded as a two-person zero-sum game. The condition under which both sides of the game will reach a Nash equilibrium is investigated.

In this dissertation, we first investigate the problem of source location estimation in wireless sensor networks (WSNs) based on quantized data in the presence of false information attacks. Using a Gaussian mixture to model the possible attacks, we develop a maximum likelihood estimator (MLE) to estimate the source location. The Cramer-Rao lower bound (CRLB) for this estimation problem is also derived. Then, the assumption that the fusion center does not have the knowledge of the attack probability and the attack noise power investigated. We assume that the attack probability and power are random variables which follow certain uniform distributions. We derive the MLE for the localization problem. The CRLB for this estimation problem is also derived. It is shown that the proposed estimator is robust in various cases with different attack probabilities and parameter mismatch. The linear state estimation problem subjected to False Information Injection is also considered. The relationship between the attacker and the defender is modeled from a minimax perspective, in which the attacker tries to maximize the cost function. On the other hand, the defender tries to optimize the detection threshold selection to minimize the cost function. We consider that the attacker will attack with deterministic bias, then we also considered the random bias. In both cases, we derive the probabilities of detection and miss, and the probability of false alarm is derived based on the Chi squared distribution. We solve the minimax optimization problem numerically for both the cases.

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's 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'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 the Kalman filter's estimation errors. To be concrete, a target tracking system is used as an example in the thesis. 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 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.

State Estimation in Electric Power Systems: A Generalized Approach provides for the first time a comprehensive introduction to the topic of state estimation at an advanced textbook level. The theory as well as practice of weighted least squares (WLS) is covered with significant rigor. Included are an in depth analysis of power flow basics, proper justification of Stott's decoupled method, observability theory and matrix solution methods. In terms of practical application, topics such as bad data analysis, combinatorial bad data analysis and multiple snap shot estimation are covered. The book caters both to the specialist as well as the newcomer to the field. State estimation will play a crucial role in the emerging scenario of a deregulated power industry. Many market decisions will be based on knowing the present state of the system accurately. State Estimation in Electric Power Systems: A Generalized Approach crystallizes thirty years of WLS state estimation theory and practice in power systems and focuses on techniques adopted by state estimation developers worldwide. The book also reflects the experience of developing industrial-grade state estimation software that is used in the USA, South America, and many other places in world.

This book discusses the latest advances in cyber-physical security and resilience of cyber-physical systems, including cyber-attack detection, isolation, situation awareness, resilient estimation and resilient control under attack. It presents both theoretical results and important applications of the methods. Security and Resilience in Cyber-Physical Systems begins by introducing the topic of cyber-physical security, covering state-of-the-art trends in both theory and applications, as well as some of the emerging methodologies and future directions for research. It then moves on to detail theoretical methods of attack detection, resilient estimation and control within cyber-physical systems, before discussing their various applications, such as power generation and distribution, autonomous systems, wireless communication networks and chemical plants. Focusing on the detection of and accommodation to cyber-attacks on cyber-physical systems, and including both estimation and artificial-intelligence-based methods, this book will be of interest to researchers, engineers and graduate students within the fields of cyber-physical security and resilient control.

Multimodal Perception and Secure State Estimation for Robotic Mobility Platforms Enables readers to understand important new trends in multimodal perception for mobile robotics This book provides a novel perspective on secure state estimation and multimodal perception for robotic mobility platforms such as autonomous vehicles. It thoroughly evaluates filter-based secure dynamic pose estimation approaches for autonomous vehicles over multiple attack signals and shows that they outperform conventional Kalman filtered results. As a modern learning resource, it contains extensive simulative and experimental results that have been successfully implemented on various models and real platforms. To aid in reader comprehension, detailed and illustrative examples on algorithm implementation and performance evaluation are also presented. Written by four qualified authors in the field, sample topics covered in the book include: Secure state estimation that focuses on system robustness under cyber-attacks Multi-sensor fusion that helps improve system performance based on the complementary characteristics of different sensors A geometric pose estimation framework to incorporate measurements and constraints into a unified fusion scheme, which has been validated using public and self-collected data How to achieve real-time road-constrained and heading-assisted pose estimation This book will appeal to graduate-level students and professionals in the fields of ground vehicle pose estimation and perception who are looking for modern and updated insight into key concepts related to the field of robotic mobility platforms.

Energy Internet, a futuristic evolution of electricity system, is conceptualized as an energy sharing network. The energy internet integrates advanced sensors, efficient measurement technologies, advanced control methods, and efficient energy utilization/conversion/storage system to achieve economical, efficient, and environmentally friendly operation of the power grid system. The energy internet also contains a large amount of heterogeneous information, which requires the support of information technology more than traditional power system design. Moreover, due to the open network environment of the energy internet, any anomaly or malicious attack in the system can bring unpredictable and significant losses to the overall grid operation.