Comprehensive Survey and Taxonomies of False Injection Attacks in Smart Grid: Attack Models, Targets, and Impacts

Smart Grid has rapidly transformed the centrally controlled power system into a massively interconnected cyber-physical system that benefits from the revolutions happening in the communications (e.g. 5G) and the growing proliferation of the Internet of Things devices (such as smart metres and intelligent electronic devices). While the convergence of a significant number of cyber-physical elements has enabled the Smart Grid to be far more efficient and competitive in addressing the growing global energy challenges, it has also introduced a large number of vulnerabilities culminating in violations of data availability, integrity, and confidentiality. Recently, false data injection (FDI) has become one of the most critical cyberattacks, and appears to be a focal point of interest for both research and industry. To this end, this paper presents a comprehensive review in the recent advances of the FDI attacks, with particular emphasis on 1) adversarial models, 2) attack targets, and 3) impacts in the Smart Grid infrastructure. This review paper aims to provide a thorough understanding of the incumbent threats affecting the entire spectrum of the Smart Grid. Related literature are analysed and compared in terms of their theoretical and practical implications to the Smart Grid cybersecurity. In conclusion, a range of technical limitations of existing false data attack research is identified, and a number of future research directions is recommended.Comment: Double-column of 24 pages, prepared based on IEEE Transaction articl

Impact Assessment, Detection, And Mitigation Of False Data Attacks In Electrical Power Systems

The global energy market has seen a massive increase in investment and capital flow in the last few decades. This has completely transformed the way power grids operate - legacy systems are now being replaced by advanced smart grid infrastructures that attest to better connectivity and increased reliability. One popular example is the extensive deployment of phasor measurement units, which is referred to PMUs, that constantly provide time-synchronized phasor measurements at a high resolution compared to conventional meters. This enables system operators to monitor in real-time the vast electrical network spanning thousands of miles. However, a targeted cyber attack on PMUs can prompt operators to take wrong actions that can eventually jeopardize the power system reliability. Such threats originating from the cyber-space continue to increase as power grids become more dependent on PMU communication networks. Additionally, these threats are becoming increasingly efficient in remaining undetected for longer periods while gaining deep access into the power networks. An attack on the energy sector immediately impacts national defense, emergency services, and all aspects of human life. Cyber attacks against the electric grid may soon become a tactic of high-intensity warfare between nations in near future and lead to social disorder. Within this context, this dissertation investigates the cyber security of PMUs that affects critical decision-making for a reliable operation of the power grid. In particular, this dissertation focuses on false data attacks, a key vulnerability in the PMU architecture, that inject, alter, block, or delete data in devices or in communication network channels. This dissertation addresses three important cyber security aspects - (1) impact assessment, (2) detection, and (3) mitigation of false data attacks. A comprehensive background of false data attack models targeting various steady-state control blocks is first presented. By investigating inter-dependencies between the cyber and the physical layers, this dissertation then identifies possible points of ingress and categorizes risk at different levels of threats. In particular, the likelihood of cyber attacks against the steady-state power system control block causing the worst-case impacts such as cascading failures is investigated. The case study results indicate that false data attacks do not often lead to widespread blackouts, but do result in subsequent line overloads and load shedding. The impacts are magnified when attacks are coordinated with physical failures of generators, transformers, or heavily loaded lines. Further, this dissertation develops a data-driven false data attack detection method that is independent of existing in-built security mechanisms in the state estimator. It is observed that a convolutional neural network classifier can quickly detect and isolate false measurements compared to other deep learning and traditional classifiers. Finally, this dissertation develops a recovery plan that minimizes the consequence of threats when sophisticated attacks remain undetected and have already caused multiple failures. Two new controlled islanding methods are developed that minimize the impact of attacks under the lack of, or partial information on the threats. The results indicate that the system operators can successfully contain the negative impacts of cyber attacks while creating stable and observable islands. Overall, this dissertation presents a comprehensive plan for fast and effective detection and mitigation of false data attacks, improving cyber security preparedness, and enabling continuity of operations

Impact Assessment, Detection, and Mitigation of False Data Attacks in Electrical Power Systems

The global energy market has seen a massive increase in investment and capital flow in the last few decades. This has completely transformed the way power grids operate - legacy systems are now being replaced by advanced smart grid infrastructures that attest to better connectivity and increased reliability. One popular example is the extensive deployment of phasor measurement units, which is referred to PMUs, that constantly provide time-synchronized phasor measurements at a high resolution compared to conventional meters. This enables system operators to monitor in real-time the vast electrical network spanning thousands of miles. However, a targeted cyber attack on PMUs can prompt operators to take wrong actions that can eventually jeopardize the power system reliability. Such threats originating from the cyber-space continue to increase as power grids become more dependent on PMU communication networks. Additionally, these threats are becoming increasingly efficient in remaining undetected for longer periods while gaining deep access into the power networks. An attack on the energy sector immediately impacts national defense, emergency services, and all aspects of human life. Cyber attacks against the electric grid may soon become a tactic of high-intensity warfare between nations in near future and lead to social disorder. Within this context, this dissertation investigates the cyber security of PMUs that affects critical decision-making for a reliable operation of the power grid. In particular, this dissertation focuses on false data attacks, a key vulnerability in the PMU architecture, that inject, alter, block, or delete data in devices or in communication network channels. This dissertation addresses three important cyber security aspects - (1) impact assessment, (2) detection, and (3) mitigation of false data attacks. A comprehensive background of false data attack models targeting various steady-state control blocks is first presented. By investigating inter-dependencies between the cyber and the physical layers, this dissertation then identifies possible points of ingress and categorizes risk at different levels of threats. In particular, the likelihood of cyber attacks against the steady-state power system control block causing the worst-case impacts such as cascading failures is investigated. The case study results indicate that false data attacks do not often lead to widespread blackouts, but do result in subsequent line overloads and load shedding. The impacts are magnified when attacks are coordinated with physical failures of generators, transformers, or heavily loaded lines. Further, this dissertation develops a data-driven false data attack detection method that is independent of existing in-built security mechanisms in the state estimator. It is observed that a convolutional neural network classifier can quickly detect and isolate false measurements compared to other deep learning and traditional classifiers. Finally, this dissertation develops a recovery plan that minimizes the consequence of threats when sophisticated attacks remain undetected and have already caused multiple failures. Two new controlled islanding methods are developed that minimize the impact of attacks under the lack of, or partial information on the threats. The results indicate that the system operators can successfully contain the negative impacts of cyber attacks while creating stable and observable islands. Overall, this dissertation presents a comprehensive plan for fast and effective detection and mitigation of false data attacks, improving cyber security preparedness, and enabling continuity of operations

Intégration de la blockchain à l'Internet des objets

L'Internet des objets (IdO) est en train de transformer l'industrie traditionnelle en une industrie intelligente où les décisions sont prises en fonction des données. L'IdO interconnecte de nombreux objets (ou dispositifs) qui effectuent des tâches complexes (e.g., la collecte de données, l'optimisation des services, la transmission de données). Toutefois, les caractéristiques intrinsèques de l'IdO entraînent plusieurs problèmes, tels que la décentralisation, une faible interopérabilité, des problèmes de confidentialité et des failles de sécurité. Avec l'évolution attendue de l'IdO dans les années à venir, il est nécessaire d'assurer la confiance dans cette énorme source d'informations entrantes. La blockchain est apparue comme une technologie clé pour relever les défis de l'IdO. En raison de ses caractéristiques saillantes telles que la décentralisation, l'immuabilité, la sécurité et l'auditabilité, la blockchain a été proposée pour établir la confiance dans plusieurs applications, y compris l'IdO. L'intégration de la blockchain a l'IdO ouvre la porte à de nouvelles possibilités qui améliorent intrinsèquement la fiabilité, la réputation, et la transparence pour toutes les parties concernées, tout en permettant la sécurité. Cependant, les blockchains classiques sont coûteuses en calcul, ont une évolutivité limitée, et nécessitent une bande passante élevée, ce qui les rend inadaptées aux environnements IdO à ressources limitées. L'objectif principal de cette thèse est d'utiliser la blockchain comme un outil clé pour améliorer l'IdO. Pour atteindre notre objectif, nous relevons les défis de la fiabilité des données et de la sécurité de l'IdO en utilisant la blockchain ainsi que de nouvelles technologies émergentes, notamment l'intelligence artificielle (IA). Dans la première partie de cette thèse, nous concevons une blockchain qui garantit la fiabilité des données, adaptée à l'IdO. Tout d'abord, nous proposons une architecture blockchain légère qui réalise la décentralisation en formant un réseau superposé où les dispositifs à ressources élevées gèrent conjointement la blockchain. Ensuite, nous présentons un algorithme de consensus léger qui réduit la puissance de calcul, la capacité de stockage, et la latence de la blockchain. Dans la deuxième partie de cette thèse, nous concevons un cadre sécurisé pour l'IdO tirant parti de la blockchain. Le nombre croissant d'attaques sur les réseaux IdO, et leurs graves effets, rendent nécessaire la création d'un IdO avec une sécurité plus sophistiquée. Par conséquent, nous tirons parti des modèles IA pour fournir une intelligence intégrée dans les dispositifs et les réseaux IdO afin de prédire et d'identifier les menaces et les vulnérabilités de sécurité. Nous proposons un système de détection d'intrusion par IA qui peut détecter les comportements malveillants et contribuer à renforcer la sécurité de l'IdO basé sur la blockchain. Ensuite, nous concevons un mécanisme de confiance distribué basé sur des contrats intelligents de blockchain pour inciter les dispositifs IdO à se comporter de manière fiable. Les systèmes IdO existants basés sur la blockchain souffrent d'une bande passante de communication et d’une évolutivité limitée. Par conséquent, dans la troisième partie de cette thèse, nous proposons un apprentissage machine évolutif basé sur la blockchain pour l'IdO. Tout d'abord, nous proposons un cadre IA multi-tâches qui exploite la blockchain pour permettre l'apprentissage parallèle de modèles. Ensuite, nous concevons une technique de partitionnement de la blockchain pour améliorer l'évolutivité de la blockchain. Enfin, nous proposons un algorithme d'ordonnancement des dispositifs pour optimiser l'utilisation des ressources, en particulier la bande passante de communication.Abstract : The Internet of Things (IoT) is reshaping the incumbent industry into a smart industry featured with data-driven decision making. The IoT interconnects many objects (or devices) that perform complex tasks (e.g., data collection, service optimization, data transmission). However, intrinsic features of IoT result in several challenges, such as decentralization, poor interoperability, privacy issues, and security vulnerabilities. With the expected evolution of IoT in the coming years, there is a need to ensure trust in this huge source of incoming information. Blockchain has emerged as a key technology to address the challenges of IoT. Due to its salient features such as decentralization, immutability, security, and auditability, blockchain has been proposed to establish trust in several applications, including IoT. The integration of IoT and blockchain opens the door for new possibilities that inherently improve trustworthiness, reputation, and transparency for all involved parties, while enabling security. However, conventional blockchains are computationally expensive, have limited scalability, and incur significant bandwidth, making them unsuitable for resource-constrained IoT environments. The main objective of this thesis is to leverage blockchain as a key enabler to improve the IoT. Toward our objective, we address the challenges of data reliability and IoT security using the blockchain and new emerging technologies, including machine learning (ML). In the first part of this thesis, we design a blockchain that guarantees data reliability, suitable for IoT. First, we propose a lightweight blockchain architecture that achieves decentralization by forming an overlay network where high-resource devices jointly manage the blockchain. Then, we present a lightweight consensus algorithm that reduces blockchain computational power, storage capability, and latency. In the second part of this thesis, we design a secure framework for IoT leveraging blockchain. The increasing number of attacks on IoT networks, and their serious effects, make it necessary to create an IoT with more sophisticated security. Therefore, we leverage ML models to provide embedded intelligence in the IoT devices and networks to predict and identify security threats and vulnerabilities. We propose a ML intrusion detection system that can detect malicious behaviors and help further bolster the blockchain-based IoT’s security. Then, we design a distributed trust mechanism based on blockchain smart contracts to incite IoT devices to behave reliably. Existing blockchain-based IoT systems suffer from limited communication bandwidth and scalability. Therefore, in the third part of this thesis, we propose a scalable blockchain-based ML for IoT. First, we propose a multi-task ML framework that leverages the blockchain to enable parallel model learning. Then, we design a blockchain partitioning technique to improve the blockchain scalability. Finally, we propose a device scheduling algorithm to optimize resource utilization, in particular communication bandwidth

Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

K-Means and Alternative Clustering Methods in Modern Power Systems

As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies

A World-Class University-Industry Consortium for Wind Energy Research, Education, and Workforce Development: Final Technical Report

During the two-year project period, the consortium members have developed control algorithms for enhancing the reliability of wind turbine components. The consortium members have developed advanced operation and planning tools for accommodating the high penetration of variable wind energy. The consortium members have developed extensive education and research programs for educating the stakeholders on critical issues related to the wind energy research and development. In summary, The Consortium procured one utility-grade wind unit and two small wind units. Specifically, the Consortium procured a 1.5MW GE wind unit by working with the world leading wind energy developer, Invenergy, which is headquartered in Chicago, in September 2010. The Consortium also installed advanced instrumentation on the turbine and performed relevant turbine reliability studies. The site for the wind unit is InvenergyÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs Grand Ridge wind farmin Illinois. The Consortium, by working with Viryd Technologies, installed an 8kW Viryd wind unit (the Lab Unit) at an engineering lab at IIT in September 2010 and an 8kW Viryd wind unit (the Field Unit) at the Stuart Field on IITÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs main campus in July 2011, and performed relevant turbine reliability studies. The operation of the Field Unit is also monitored by the Phasor Measurement Unit (PMU) in the nearby Stuart Building. The Consortium commemorated the installations at the July 20, 2011 ribbon-cutting ceremony. The ConsortiumÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs researches on turbine reliability included (1) Predictive Analytics to Improve Wind Turbine Reliability; (2) Improve Wind Turbine Power Output and Reduce Dynamic Stress Loading Through Advanced Wind Sensing Technology; (3) Use High Magnetic Density Turbine Generator as Non-rare Earth Power Dense Alternative; (4) Survivable Operation of Three Phase AC Drives in Wind Generator Systems; (5) Localization of Wind Turbine Noise Sources Using a Compact Microphone Array; (6) Wind Turbine Acoustics - Numerical Studies; and (7) Performance of Wind Turbines in Rainy Conditions. The ConsortiumÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs researches on wind integration included (1) Analysis of 2030 Large-Scale Wind Energy Integration in the Eastern Interconnection; (2) Large-scale Analysis of 2018 Wind Energy Integration in the Eastern U.S. Interconnection; (3) Integration of Non-dispatchable Resources in Electricity Markets; (4) Integration of Wind Unit with Microgrid. The ConsortiumÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs education and outreach activities on wind energy included (1) Wind Energy Training Facility Development; (2) Wind Energy Course Development; (3) Wind Energy Outreach