A smart grid is an electricity grid that allows two-way flow of electricity in its network, enabling consumers to have better control over their electricity usage while reducing the operations and management costs for utilities. The communication devices in smart grids have increased the integration of renewable energy systems, such as wind and solar, and have proven to be very effective at helping restore power faster when a power disturbance occurs. In recent years, the integration of more communication devices in the power grid has opened the opportunity for more Data Integrity cyber-physical attacks. Smart grids can be a prime target for these types of attacks which can lead to cascading failures in a transmission system. False Data Injection attacks, a type of Data Integrity cyber-physical attack, can manipulate the system's measurements, and therefore, the power dispatch, in a way that can make the lines in the system overflow. This type of attack could theoretically be performed without the operator ever knowing that there was an attack, and it can cause power outages and even system blackouts. The purpose of this thesis is to implement a False Data Injection attack strategy on targeted buses that bypass DC state estimation and develop a new algorithm that can detect them using AC state estimation with Kalman Filters. Possible attacks on the system will be considered and Kalman Filters will be used to aid in the detection of bad data injections in the system that would allow the operator to know if there is an attack currently happening. The proposed novel algorithm was developed in MATLAB and tested using a modified IEEE 14 bus-system with a fixed power flow between lines of 25 MW.

​This book discusses cybersecurity issues of smart grid cyber-physical systems, focusing on the detection techniques against false data injection attacks. The authors discuss passive and proactive techniques that combat and mitigate two categories of false data injection attacks, false measurement data injections and false command data injections in smart grid cyber-physical systems. These techniques are easy to follow for either professionals or beginners. With this book, readers can quickly get an overview of this topic and get ideas of new solutions for false data injections in smart grid cyber-physical systems. Readers include researchers, academics, students, and professionals. Presents a comprehensive summary for the detection techniques of false data injection attacks in smart grid cyber-physical systems; Reviews false data injections for either measurement data or command data; Analyzes passive and proactive approaches to smart grid cyber-physical systems.

Smart Grid: Networking, Data Management, and Business Models delivers a comprehensive overview of smart grid communications, discussing the latest advances in the technology, the related cyber security issues, and the best ways to manage user demand and pricing. Comprised of 16 chapters authored by world-renowned experts, this book: Considers the use of cognitive radio and software-defined networking in the smart grid Explores the space of attacks in the energy management process, the need for a smart grid simulator, and the management issues that arise around smart cities Describes a real-time pricing scheme that aims to reduce the peak-to-average load ratio Explains how to realize low-carbon economies and the green smart grid through the pervasive management of demand Presents cutting-edge research on microgrids, electric vehicles, and energy trading in the smart grid Thus, Smart Grid: Networking, Data Management, and Business Models provides a valuable reference for utility operators, telecom operators, communications engineers, power engineers, electric vehicle original equipment manufacturers (OEMs), electric vehicle service providers, university professors, researchers, and students.

A groundbreaking book from Simon Haykin, setting out the fundamental ideas and highlighting a range of future research directions.

This book analyzes the secure problems of cyber-physical systems from both the adversary and defender sides. Targeting the challenging security problems of cyber-physical systems under malicious attacks, this book presents some recent novel secure state estimation and control algorithms, in which moving target defense scheme, zero-sum game-theoretical approach, reinforcement learning, neural networks, and intelligent control are adopted. Readers will find not only the valuable secure state estimation and control schemes combined with the approaches aforementioned, but also some vital conclusions for securing cyber-physical systems, for example, the critical value of allowed attack probability, the maximum number of sensors to be attacked, etc. The book also provides practical applications, example of which are unmanned aerial vehicles, interruptible power system, and robot arm to validate the proposed secure algorithms. Given its scope, it offers a valuable resource for undergraduate and graduate students, academics, scientists, and engineers who are working in this field.

This book covers power systems cybersecurity. In order to enhance overall stability and security in wide-area cyber-physical power systems and defend against cyberattacks, new resilient operation, control, and protection methods are required. The cyberattack-resilient control methods improve overall cybersecurity and stability in normal and abnormal operating conditions. By contrast, cyberattack-resilient protection schemes are important to keep the secure operation of a system under the most severe contingencies and cyberattacks. The main subjects covered in the book are: 1) proposing new tolerant and cyberattack-resilient control and protection methods against cyberattacks for future power systems, 2) suggesting new methods for cyberattack detection and cybersecurity assessment, and 3) focusing on practical issues in modern power systems.

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.