​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.

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 focuses on the combined cyber and physical security issues in advanced electric smart grids. Existing standards are compared with classical results and the security and privacy principles of current practice are illustrated. The book paints a way for future development of advanced smart grids that operated in a peer-to-peer fashion, thus requiring a different security model. Future defenses are proposed that include information flow analysis and attestation systems that rely on fundamental physical properties of the smart grid system.

This book presents a comprehensive overview of security issues in Cyber Physical Systems (CPSs), by analyzing the issues and vulnerabilities in CPSs and examining state of the art security measures. Furthermore, this book proposes various defense strategies including intelligent attack and anomaly detection algorithms. Today’s technology is continually evolving towards interconnectivity among devices. This interconnectivity phenomenon is often referred to as Internet of Things (IoT). IoT technology is used to enhance the performance of systems in many applications. This integration of physical and cyber components within a system is associated with many benefits; these systems are often referred to as Cyber Physical Systems (CPSs). The CPSs and IoT technologies are used in many industries critical to our daily lives. CPSs have the potential to reduce costs, enhance mobility and independence of patients, and reach the body using minimally invasive techniques. Although this interconnectivity of devices can pave the road for immense advancement in technology and automation, the integration of network components into any system increases its vulnerability to cyber threats. Using internet networks to connect devices together creates access points for adversaries. Considering the critical applications of some of these devices, adversaries have the potential of exploiting sensitive data and interrupting the functionality of critical infrastructure. Practitioners working in system security, cyber security & security and privacy will find this book valuable as a reference. Researchers and scientists concentrating on computer systems, large-scale complex systems, and artificial intelligence will also find this book useful as a reference.

Cyber-Physical Attacks: A Growing Invisible Threat presents the growing list of harmful uses of computers and their ability to disable cameras, turn off a building’s lights, make a car veer off the road, or a drone land in enemy hands. In essence, it details the ways cyber-physical attacks are replacing physical attacks in crime, warfare, and terrorism. The book explores how attacks using computers affect the physical world in ways that were previously only possible through physical means. Perpetrators can now cause damage without the same risk, and without the political, social, or moral outrage that would follow a more overt physical attack. Readers will learn about all aspects of this brave new world of cyber-physical attacks, along with tactics on how to defend against them. The book provides an accessible introduction to the variety of cyber-physical attacks that have already been employed or are likely to be employed in the near future. Demonstrates how to identify and protect against cyber-physical threats Written for undergraduate students and non-experts, especially physical security professionals without computer science background Suitable for training police and security professionals Provides a strong understanding of the different ways in which a cyber-attack can affect physical security in a broad range of sectors Includes online resources for those teaching security management

The Smart Grid (SG) is an upgraded, intelligent, and a more reliable version of the traditional Power Grid due to the integration of information and communication technologies. The operation of the SG requires a dense communication network to link all its components. But such a network renders it prone to cyber attacks jeopardizing the integrity and security of the communicated data between the physical electric grid and the control centers. One of the most prominent components of the SG are Wide Area Monitoring Systems (WAMS). WAMS are a modern platform for grid-wide information, communication, and coordination that play a major role in maintaining the stability of the grid against major disturbances. In this thesis, an anomaly detection framework is proposed to identify False Data Injection (FDI) attacks in WAMS using different Machine Learning (ML) and Deep Learning (DL) techniques, i.e., Deep Autoencoders (DAE), Long-Short Term Memory (LSTM), and One-Class Support Vector Machine (OC-SVM). These algorithms leverage diverse, complex, and high-volume power measurements coming from communications between different components of the grid to detect intelligent FDI attacks. The injected false data is assumed to target several major WAMS monitoring applications, such as Voltage Stability Monitoring (VSM), and Phase Angle Monitoring (PAM). The attack vector is considered to be smartly crafted based on the power system data, so that it can pass the conventional bad data detection schemes and remain stealthy. Due to the lack of realistic attack data, machine learning-based anomaly detection techniques are used to detect FDI attacks. To demonstrate the impact of attacks on the realistic WAMS traffic and to show the effectiveness of the proposed detection framework, a Hardware-In-the-Loop (HIL) co-simulation testbed is developed. The performance of the implemented techniques is compared on the testbed data using different metrics: Accuracy, F1 score, and False Positive Rate (FPR) and False Negative Rate (FNR). The IEEE 9-bus and IEEE 39-bus systems are used as benchmarks to investigate the framework scalability. The experimental results prove the effectiveness of the proposed models in detecting FDI attacks in WAMS.