DIGITAL TWINS IN NUCLEAR POWER: CYBERSECURITY IMPLICATIONS
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The electric power grid is increasingly becoming susceptible to cyber attacks that exploit vulnerabilities in the smart grid control, information, and physical layers. Successful cyber attacks can have catastrophic impacts on the social and economic well-being of any nation all over the globe. It has, thus, become imperative to secure the smart grid against such adversarial actions to ensure stable, secure, and reliable operation of the grid. The existing research and industry practices prove to be inadequate in terms of providing pragmatic and effective defense methodologies and measures for long-term cybersecurity planning and real-time cybersecurity for grid operation. For example, existing works lack models that incorporate uncertain behavior of cyber-attackers and pragmatic defense measures for cyber risk assessment and cybersecurity investment optimization which often provide unreliable and strictly qualitative solutions to these problems. At the same time, with the growing number of cyber incidents in the grid, there still exists a need to develop attack-resilient algorithms for wide-area monitoring, protection, and control (WAMPAC) applications like the wide-area voltage control systems (WAVCS) for Flexible AC Transmissions Systems (FACTS) that lack in scalable and feasible solutions from the cybersecurity perspective. This dissertation proposes novel models and methodologies for: (1) Cybersecurity planning, and (2) Cybersecurity for system operation. The cybersecurity planning is achieved through cyber risk assessment and cybersecurity resource investment optimization for long-term cybersecurity of the grid using game theory and attack-defense trees. Cybersecurity for system operation consists of development of cyber anomaly detection and mitigation algorithms for flexible AC transmission system (FACTS) controller-based wide-area voltage control systems (WAVCS) using machine learning (ML), and software defined networking-based moving target defense network routing for achieving real-time cyber-physical security for grid operations. This is followed by hardware-in-the-loop (HIL) implementation and evaluation of these attack prevention, detection, and mitigation algorithms and methodologies showcasing their feasibility in a close to real-world environment. For cybersecurity planning, a novel approach involving a combination of game theory and attack defense trees (ADT) for optimal cybersecurity resource allocation in the smart grid is proposed. This methodology involves modeling of the cyber-physical smart grid substations as ADTs, defining attacker costs, defense costs, and attack probabilities for attack access points. Using game theoretical formulation, optimal defense strategies for the defender of the system to invest cybersecurity resources in the grid are obtained. Additionally, a game-theoretic framework is developed for quantitative cyber-physical risk assessment of the grid under a dynamically changing cyber threat space and uncertain behavior of cyber attackers which is further used to optimize investments in the smart grid's cybersecurity resources. The attacker, defender, and the smart grid system are modeled while incorporating attacker-stochasticity and federal guidelines for smart grid cybersecurity. This allows quantification of threat, vulnerabilities, and attack impact of the grid for quantitative risk assessment. The defender's budget to invest in the security resources in the grid is optimized based on the strategies leading to minimum system risk. The evaluation of the proposed solutions highlight the feasibility for practical implementation of these methodologies and algorithms in the smart grid, while taking the federal requirements and guidelines for smart grid security into consideration. For achieving cybersecurity for system operation, attack prevention, detection, and mitigation algorithms and methodologies are developed specifically for FACTS-based WAVCS. Anomaly detection and mitigation in the WAVCS are achieved using algorithms based on machine learning which involves offline training and testing of ML models with CPS datasets incorporating physics-based features that allow accurate distinction between system faults and cyber attacks. For attack prevention, a methodology based on software defined network (SDN)-based moving target defense (MTD) network routing is proposed that enables prevention of Denial of Service (DoS) type attacks on the smart grid communication system. Subsequently, these methodologies and algorithms are implemented and evaluated on an HIL testbed that allows for real-time attack prevention, detection, and mitigation of emulated cyber attacks on the WAVCS in a close to real-world environment. The results show highly accurate and efficient performance of the implemented algorithms and methodologies with the smart grid system operating within the NERC's system operation limits even in the presence of DoS and data integrity cyber attacks. This work opens up future research opportunities in other directions such as (1) Expanding cybersecurity planning methodologies to real-time cyber contingency analysis with different game formulations; and (2) Applying the cybersecurity for system operation algorithms to broader categories of wide-area control applications.
As devices and systems continue to modernize and adopt integrated circuits, the use of cyber technology to deploy an application is the expectation. This deployment through cyber assets brings new cyber risk and cybersecurity is the practice of managing this risk. Cyber-risk is constantly changing due to the speed of technology advancement and the changing quality of the adversary. Cyber-Informed Engineering (CIE) mitigates cyber-risk through engineering controls where as the traditional practice of cybersecurity mitigates cyber-risk through cybersecurity controls. By clearly defining the cyber-physical boundary, engineering controls and cybersecurity controls can clearly demonstrate their complementary nature to provide layered defenses and successfully mitigate cyber-risk through independent controls. In this paper, a layered model of device decomposition of the the cyber-physical boundary is presented to provide clarity where engineering controls are used to reduce cyber-risk within the physics, functional materials, electronic, or integrated circuit layers and where cybersecurity controls are used to reduce cyber-risk within the machine code and application layers. By implementing both traditional cybersecurity controls and engineering controls, a more holistic approach to cybersecurity is achieved in protecting modern devices and systems, as well as a clear awareness in identifying, documenting, and authorizing the system’s cybersecurity protection scheme is achieved.
Market conditions are forcing fossil electricity generation facility owners and operators to implement advanced digital technologies. These technologies enable efficiencies, operational flexibility, operations and maintenance efficiencies, and adapting to a transitioning workforce. These digital technologies, however, can increase the cybersecurity attack surface. The purpose of this research was to develop a holistic cybersecurity risk reduction framework for fossil generation facilities. The framework begins with assessing how cyber risk changes across facility life cycles, including plant, system, vendor, and business life cycles. The next phase performs consequence analysis to prioritize high consequence events. Focusing on high consequence events allows owners to use a graded, risk-informed approach to prioritize cybersecurity efforts. The final phase identifies the digital asset attack surface in sensors and instrumentation and control equipment. After the vulnerabilities are identified, the owner selects mitigating cybersecurity control measures (or countermeasures) based on the risk analysis from the previous phases. This report describes the current industry cybersecurity best practices in fossil generation that are based on the first principles for cybersecurity engineering. The report is divided into five sections that describe the implementation of the risk reduction framework and present identified research, methodological, and technology gaps that were identified through this course of research and development.
The growth of utility-scale renewable energy resources, distributed energy resources (DER), and transportation electrification has increased uncertainty and cybersecurity risks in power grids. The Purdue Enterprise Reference Architecture model which is widely adopted by the utility industry is now insufficient to protect the power grid against cyber-attacks. There is a need to identify what cybersecurity model is effective on Energy Management System (EMS), Advanced Distribution Management System (ADMS), and DER Management System (DERMS) to address the fundamental cybersecurity challenges in the age of increasing renewable energy and DER share as well as consumer participation in the electric energy industry. The next generation of cybersecurity model for OT network should be able to detect inside attackers, mitigate the cybersecurity risks arising from the new grid participants including DER aggregators, electric vehicle owners, and behind-the-meter consumers outside the utility company, and develop the strategy to trust consumer measurement data. This panel will discuss the challenges and pathways for the development of an ensemble cybersecurity model based on predictive state estimation to detect cybersecurity anomalies in OT network including EMS, ADMS, and DERMS.
Boise State University’s (BSU) Cyber Operations and Resilience CORe program was intentionally designed so that any student, especially non-traditional and non-technical students, with an interest in cybersecurity could have an education and training pathway to enter the cyber workforce. The CORe curriculum focuses on teaching students how to design, apply, and improve cybersecurity through the interaction of people, processes, and technology. CORe is a stackable curriculum with elective credit hours and options for various academic and industry certificates and certifications that enable students to customize their unique career pathway. The CORe program guides students to think about the system being managed, the risks presented, and the dynamic intersection of system elements when considering how to incorporate resilience frameworks in achieving a resilient system. By developing systems thinking, the students gain an understanding of the interdependencies interacting with the operational system. Further, the CORe program encourages students to integrate cybersecurity knowledge with models and frameworks found in other academic disciplines through a unifying systems approach. CORe is designed around the realities of today’s broad cyber landscape: that breaches will occur in any system over time and proactive design of resilience into systems to detect, respond, and recover in a timely and orderly manner is critical. Students are taught to think holistically about cybersecurity focusing on all system elements. CORe is not a traditional cybersecurity degree. CORe is distinguished by the non-traditional engineering, computer science approach to cybersecurity education with the singular focus on infusing resilience operations and transdisciplinary systems thinking principles throughout the curriculum.
Market conditions are forcing fossil electricity generation facility owners and operators to implement advanced digital technologies. These technologies enable efficiencies, operational flexibility, operations and maintenance efficiencies, and adapting to a transitioning workforce. These digital technologies, however, can increase the cybersecurity attack surface. The purpose of this research was to develop a holistic cybersecurity risk reduction framework for fossil generation facilities. The framework begins with assessing how cyber risk changes across facility life cycles, including plant, system, vendor, and business life cycles. The next phase performs consequence analysis to prioritize high consequence events. Focusing on high consequence events allows owners to use a graded, risk-informed approach to prioritize cybersecurity efforts. The final phase identifies the digital asset attack surface in sensors and instrumentation and control equipment. After the vulnerabilities are identified, the owner selects mitigating cybersecurity control measures (or countermeasures) based on the risk analysis from the previous phases. This report describes the current industry cybersecurity best practices in fossil generation that are based on the first principles for cybersecurity engineering. The report is divided into five sections that describe the implementation of the risk reduction framework and present identified research, methodological, and technology gaps that were identified through this course of research and development.
Market conditions are forcing fossil electricity generation facility owners and operators to implement advanced digital technologies. These technologies enable efficiencies, operational flexibility, operations and maintenance efficiencies, and adapting to a transitioning workforce. These digital technologies, however, can increase the cybersecurity attack surface. The purpose of this research was to develop a holistic cybersecurity risk reduction framework for fossil generation facilities. The framework begins with assessing how cyber risk changes across facility life cycles, including plant, system, vendor, and business life cycles. The next phase performs consequence analysis to prioritize high consequence events. Focusing on high consequence events allows owners to use a graded, risk-informed approach to prioritize cybersecurity efforts. The final phase identifies the digital asset attack surface in sensors and instrumentation and control equipment. After the vulnerabilities are identified, the owner selects mitigating cybersecurity control measures (or countermeasures) based on the risk analysis from the previous phases. This report describes the current industry cybersecurity best practices in fossil generation that are based on the first principles for cybersecurity engineering. The report is divided into five sections that describe the implementation of the risk reduction framework and present identified research, methodological, and technology gaps that were identified through this course of research and development.
Industrial Control Systems device manufacturers frequently add new features to improve their product performance. Oftentimes, these changes are mainly vendor-driven initiatives, and customers may not be aware of the full impact of these new capabilities on their cybersecurity posture. In the energy sector, this can lead to considerable dissonance between vendor-provided cybersecurity claims and a customer’s responsibility for Operation Technology cybersecurity compliance. Thus, the resulting dynamic verification burden is shifted towards the customer and may pose a significant cybersecurity risk to the energy sector landscape. We found that there is very limited research into cybersecurity auditing for Operational Technology. However, a solution is needed for vetting the vendor-supplied feature claims and their adherence to cybersecurity requirements and standards. We are presently engaged in an effort to develop such a system. This paper demonstrates one vital aspect of this effort in proposing an end-to-end framework to accumulate a large repository of ICS device information for this vetting system, curate the dataset, and conduct extensive processing. This framework is designed to use web scraping, data analytics and Natural Language Processing (NLP) techniques to identify vendor websites, automate the collection of website-accessible documents and automatically derive metadata from them for identification of product documents relevant to the repository. We have found that this automated approach to vendor identification, document extraction into a product repository, and NLP pre-processing is unique and has not been previously presented in the literature. The preliminary work shows that this is feasible and can produce reliable results with minimum supervision. Future work will be built upon this foundation in order to achieve semi-supervised vetting of device technical information – a vital capability for ensuring that vendor-claimed device cybersecurity capabilities match industry requirements.
A cybersecurity infrastructure command validation system is provided herein for validating asset commands issued within an infrastructure network. The cybersecurity infrastructure command validation system can be integrated into an infrastructure network to monitor and validate infrastructure asset commands in real-time or while the infrastructure network is active. The cybersecurity infrastructure command validation system can receive or intercept commands issued by asset controllers. The cybersecurity infrastructure command validation system can validate the commands based on a command validation model. The command validation model can represent normal operating behavior of the infrastructure network. The cybersecurity infrastructure command validation system can provide valid commands to the intended infrastructure asset, or can reject invalid commands. The cybersecurity infrastructure command validation system can store validation results for use in updating the command validation model. The cybersecurity infrastructure command validation system can flag or otherwise warn the infrastructure network or administrators of invalid commands.
CIE is an emerging method to integrate cybersecurity considerations into the conception, design, development, and operation of any physical system that has digital connectivity, monitoring, or control. CIE complements—but does not replace—the application of cybersecurity standards or practices currently in place within an organization. Rather, it expands cybersecurity decisions into the engineering space, not by asking engineers to become cyber experts, but by calling on engineers to apply engineering tools and make engineering decisions that improve cybersecurity outcomes. CIE examines the engineering consequences that a sophisticated cyber attacker could achieve, and drives engineering changes that may provide deterministic mitigations to limit or eliminate those consequences. Engineers and technicians that design critical energy infrastructure installations can integrate the 12 principles of CIE into each phase of the engineering lifecycle, from concept to retirement. These principles are aimed at system or design engineers, operators, and technicians, rather than software engineers or operational cybersecurity practitioners, because the engineers who design, build, operate, and maintain the physical infrastructure are best positioned to leverage a system’s engineering design to diminish the severity of cyber attacks or digital technology failures. This approach creates new opportunities for engineering teams—and not just cybersecurity teams—to secure the system using the physics and mechanics of engineering controls—not just digital monitoring and controls.