Methodologies synthesis

This deliverable deals with the modelling and analysis of interdependencies between critical infrastructures, focussing attention on two interdependent infrastructures studied in the context of CRUTIAL: the electric power infrastructure and the information infrastructures supporting management, control and maintenance functionality. The main objectives are: 1) investigate the main challenges to be addressed for the analysis and modelling of interdependencies, 2) review the modelling methodologies and tools that can be used to address these challenges and support the evaluation of the impact of interdependencies on the dependability and resilience of the service delivered to the users, and 3) present the preliminary directions investigated so far by the CRUTIAL consortium for describing and modelling interdependencies

Formalizing Cyber--Physical System Model Transformation via Abstract Interpretation

Model transformation tools assist system designers by reducing the labor--intensive task of creating and updating models of various aspects of systems, ensuring that modeling assumptions remain consistent across every model of a system, and identifying constraints on system design imposed by these modeling assumptions. We have proposed a model transformation approach based on abstract interpretation, a static program analysis technique. Abstract interpretation allows us to define transformations that are provably correct and specific. This work develops the foundations of this approach to model transformation. We define model transformation in terms of abstract interpretation and prove the soundness of our approach. Furthermore, we develop formalisms useful for encoding model properties. This work provides a methodology for relating models of different aspects of a system and for applying modeling techniques from one system domain, such as smart power grids, to other domains, such as water distribution networks.Comment: 8 pages, 4 figures; to appear in HASE 2019 proceeding

Model-based Safety and Security Co-analysis: a Survey

We survey the state-of-the-art on model-based formalisms for safety and security analysis, where safety refers to the absence of unintended failures, and security absence of malicious attacks. We consider ten model-based formalisms, comparing their modeling principles, the interaction between safety and security, and analysis methods. In each formalism, we model the classical Locked Door Example where possible. Our key finding is that the exact nature of safety-security interaction is still ill-understood. Existing formalisms merge previous safety and security formalisms, without introducing specific constructs to model safety-security interactions, or metrics to analyze trade offs

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

Cyber-Physical Power System Layers: Classification, Characterization, and Interactions

This paper provides a strategy to identify layers and sub-layers of cyber-physical power systems (CPPS) and characterize their inter- and intra-actions. The physical layer usually consists of the power grid and protection devices whereas the cyber layer consists of communication, and computation and control components. Combining components of the cyber layer in one layer complicates the process of modeling intra-actions because each component has different failure modes. On the other hand, dividing the cyber layers into a large number of sub-layers may unnecessarily increase the number of system states and increase the computational burden. In this paper, we classify system layers based on their common, coupled, and shared functions. Also, interactions between the classified layers are identified, characterized, and clustered based on their impact on the system. Furthermore, based on the overall function of each layer and types of its components, intra-actions within layers are characterized. The strategies developed in this paper for comprehensive classification of system layers and characterization of their inter- and intra-actions contribute toward the goal of accurate and detailed modeling of state transition and failure and attack propagation in CPPS, which can be used for various reliability assessment studies.Comment: Accepted in Texas Power and Energy Conference (TPEC) 202