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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Performing Numerical Analysis of Cybersecurity Options Using Dynamic Risk Analysis Tool EMRALD

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Considering a cyber threat should involve defense-in-depth methods and a quantitative or numerical evaluation of overall effectiveness against dynamic, time-dependent attacks to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related safety is a requirement set by North American Electric Reliability and the U.S. Nuclear Regulatory Commission. They are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks may focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want to know business reliability and recovery from those threats, and that requires modeling physical behavior of the targets. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with different tools having issues such as state-base explosion. Dynamic modeling enables time and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic numerical risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies. Keywords: cyber modeling; cyber-physical systems; numerical cyber modeling

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Implementation of an ICS Ransomware Testbed: Scenarios, Variants, and Evaluation Methods

Ransomware attacks on Industrial Control Systems (ICS) have emerged as a formidable threat to the United States’ critical infrastructure, eliciting grave concerns regarding national security. In March 2023, the FBI Internet Crime Complaint Center (IC3) unveiled its 2022 Internet Crime Report, highlighting a concerning 870 complaints related to ransomware impacting U.S. critical infrastructure. Of the country's 16 critical infrastructure sectors, 14 encountered at least one ransomware attack. Notably, while the Healthcare and Public Health sector suffered the most, reporting 210 attacks, sectors pivotal to ICS networks and governmental organizations were also targeted: the Defense Industrial Base reported 1 attack, Water and Wastewater Systems 3, Chemical 19, Energy 15, Government Facilities 115, and Critical Manufacturing 157. For instance, a ransomware attack on a major chemical company could jeopardize not only its production but also pose environmental risks should systems controlling hazardous materials be compromised. In 2022, three ransomware variants predominantly targeted U.S. critical infrastructure: HIVE, with 87 attacks; ALPHV/BlackCat, with 114; and LOCKBIT, with 149. Several cyber-attacks, such as the MOVEit data breach in May 2023 and the Colonial Pipeline ransomware attack in May 2021, have been so impactful that they commanded national attention. The DarkSide hacking group's assault on the Colonial Pipeline, initiated on May 6th, 2021, stands as one of the most substantial and publicly acknowledged cyber-attacks against U.S. critical infrastructure. The group exploited an exposed Virtual Private Network (VPN) password, paving the way for initial intrusion and subsequent data theft. A mere day later, DarkSide unleashed a ransomware attack that compromised vital accounting and billing systems, prompting an immediate shutdown of the pipeline to mitigate further ransomware proliferation across its network. This crisis spurred a robust response from the U.S. president and regulators, culminating in a national emergency declaration related to the pipeline shutdown on May 9th, 2021. This incident mirrors the 2017 NotPetya ransomware attack that significantly impacted the shipping giant Maersk, highlighting an urgent need for fortified cybersecurity across various industries. Future incidents, akin to the Colonial Pipeline attack, could potentially be mitigated—or entirely averted—should government agencies and private entities scrutinize system vulnerabilities, exploring various ransomware types and entry points. Proactive measures, such as conducting experiments on VPN accounts or auditing passwords to pinpoint duplicate usage across diverse systems and software, might illuminate feasible entry points and vulnerability zones within an organization's systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Enhancing Early Systems R&D Capabilities with Systems —Theoretic Process Analysis

Systems engineering today faces a wide array of challenges, ranging from new operational environments to disruptive technological — necessitating approaches to improve research and development (R&D) efforts. Yet, emphasizing the Aristotelian argument that the “whole is greater than the sum of its parts” seems to offer a conceptual foundation creating new R&D solutions. Invoking systems theoretic concepts of emergence and hierarchy and analytic characteristics of traceability, rigor, and comprehensiveness is potentially beneficial for guiding R&D strategy and development to bridge the gap between theoretical problem spaces and engineering-based solutions. In response, this article describes systems–theoretic process analysis (STPA) as an example of one such approach to aid in early-systems R&D discussions. STPA—a ‘top-down’ process that abstracts real complex system operations into hierarchical control structures, functional control loops, and control actions—uses control loop logic to analyze how control actions (designed for desired system behaviors) may become violated and drive the complex system toward states of higher risk. By analyzing how needed controls are not provided (or out of sequence or stopped too soon) and unneeded controls are provided (or engaged too long), STPA can help early-system R&D discussions by exploring how requirements and desired actions interact to either mitigate or potentially increase states of risk that can lead to unacceptable losses. This article will demonstrate STPA's benefit for early-system R&D strategy and development discussion by describing such diverse use cases as cyber security, nuclear fuel transportation, and US electric grid performance. Together, the traceability, rigor, and comprehensiveness of STPA serve as useful tools for improving R&D strategy and development discussions. In conclusion, leveraging STPA as well as related systems engineering techniques can be helpful in early R&D planning and strategy development to better triangulate deeper theoretical meaning or evaluate empirical results to better inform systems engineering solutions.

42 ENGINEERING↗
DOE OSTI2023

Cyber threat assessment of machine learning driven autonomous control systems of nuclear power plants

We report advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)-based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber–physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Survey of Cyber Risk Analysis Techniques for Use in the Nuclear Industry

Using traditional probabilistic risk analysis methods for severe accident safety risk management on non-digital systems, structures, and components at nuclear power plants is well-established. In contrast, cyber risk analysis of digital assets is still an immature field with unproven techniques due, in part, to the continuously changing threat environment and the challenge of digital assets failing in unexpected ways. As the nuclear fleet continues to adopt digital instrumentation and control systems, it is increasingly important to have effective and efficient cyber risk analysis techniques to support risk management decisions, such as risk elimination by system redesign or risk mitigation by implementation of prioritized security controls. To understand the state of the art in cyber risk analysis for future research, we surveyed 36 publications across ten application domains. We describe our survey methodology and rate each technique based upon scope, adoptability, and repeatability. In this work, we examine the unique constraints of the nuclear industry and outline the strengths and weaknesses of using the cyber risk analysis techniques in the industry, highlighting gaps with current techniques. We also discuss challenges and potential research directions for advancing the science for both existing and new advanced reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Defensive Cybersecurity Architecture Design Using Force-on-Force Cyber-Physical Modeling

Currently, nuclear power plant physical security systems are highly dependent on air-gaps as a protective measure against cyber-threats. Cyber-physical threats become more likely as advanced cyber-threat capabilities to jump air-gaps transition into common use. Defending against the emerging threat of cyber-enabled physical intrusions is poorly understood. The consequence of these cyber-physical attacks has no quantitative analysis method to inform risk-informed, performance-based cybersecurity approaches. By modifying the physical security simulation tool Dante, cyber-physical threat consequence was able to be analyzed on a notional facility. The results of this analysis are used to design a Defensive Cybersecurity Architecture (DCSA) for the physical security system to produce example resilience measures for this notional facility. A DCSA defines security levels to provide a graded approach for defending plant functions, and security zones for trusted communication between systems. This approach can be applied to real world systems to produce physical protection systems and response measures that are resilient to cyber-physical threats.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

IMPLEMENTING CYBERSECURITY INTO AN EXISTING NATIONAL NUCLEAR NON-PROLIFERATION PROGRAMME – A CASE STUDY

Cyber threat profiling and risk mitigation is critical to any nuclear state organization and should be considered as part of any comprehensive nuclear security programme. Defining and evaluating the impact of the cyber threat to mission can be challenging. An existing national nuclear non-proliferation organization undertook an effort to incorporate computer security activities into its programme to address cyber risk. One of the primary goals of this endeavour was to develop a set of prioritised recommendations for organizational follow-through. The organization dedicated subject matter expert resources in the form of a cyber task force to support this goal. Opportunities were identified where computer security could be built into each programme including office-level strategies and tools. Of course, no new identified threat vector is easily considered and incorporated into existing programmes without impact. There are many obstacles to be overcome. Technically literate subject matter experts are difficult to find, management has comparatively less experience applying computer security into its programmes, and trying to change the culture to consider computer security risk at policy and programmatic levels takes time and management attention. As an outcome of this process, a roadmap for program integration was developed, including the establishment of a cyber support team. This paper will discuss the challenges and successes associated with establishing such a team.

Cyber Security, Nuclear, Non-proliferation↗
DOE OSTI2022

The evolution of the Human Systems and Simulation Laboratory in nuclear power research

The events at Three Mile Island in the United States brought about fundamental changes in the ways that simulation would be used in nuclear operations. The need for research simulators was identified to scientifically study human-centered risk and make recommendations for process control system designs. This paper documents the human factors research conducted at the Human Systems and Simulation Laboratory (HSSL) since its inception in 2010 at Idaho National Laboratory. The facility’s primary purposes are to provide support to utilities for system upgrades and to validate modernized control room concepts. In the last decade, however, as nuclear industry needs have evolved, so too have the purposes of the HSSL. Thus, beyond control room modernization, human factors researchers have evaluated the security of nuclear infrastructure from cyber adversaries and evaluated human-in-the-loop simulations for joint operations with an integrated hydrogen generation plant. Lastly, our review presents research using human reliability analysis techniques with data collected from HSSL-based studies and concludes with potential future directions for the HSSL, including severe accident management and advanced control room technologies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Twenty Years and Counting—Where are they? Practical Recommendations for Commercializing AI/ML for Intrusion Detection in the Nuclear Industry

Research and development into applications for improving equipment condition monitoring programs at nuclear facilities has been around since the 1990s. However, while the field has moved from using data-driven machine learning (ML) algorithms for detection and prediction of equipment degradation and failure to prognostic capabilities, these applications are still not widely used in the U.S. nuclear industry. Additionally, there has been significant effort in designing both data-driven and physics-based artificial intelligence (AI) and ML models for many other potential applications in the nuclear industry, including cyber intrusion detection systems (IDS). However, as the last twenty years in condition-based maintenance research has shown us, there are significant hurdles that must be overcome for deployment of IDS on plant systems. This paper provides a discussion on the practical recommendations that researchers should consider for successful adoption of AI/ML IDS in the nuclear industry.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Complete Evaluation on Advanced Reactor Machine Learning Subversion Attacks (Final)

Navigating through the world of Artificial Intelligence (AI) in nuclear reactors and their Instrumentation and Control (I&C) systems demands a careful, deliberate journey. AI’s capability to manage massive datasets and streamline control systems has indeed carved out a significant role in various sectors, including nuclear energy. However, while AI, and particularly Large Language Models (LLMs), bring a lot to the table in terms of operational efficiency and anomaly detection, they also expose the sector to a new breed of cybersecurity threats, like Inference Attacks, Adversarial Attacks, and Trojan Attacks. This guide is designed to be a straightforward manual, diving deep into the intertwining worlds of AI and cybersecurity within nuclear reactors, and tailoring insights for three crucial audiences: I&C Vendors/Developers, Nuclear Regulators, and Nuclear Reactor Operators and Cyber Defense Teams. (1) Section 2, directed at I&C Vendors/Developers, will provide a clear and focused look at several cybersecurity attacks, offering practical recommendations and detailed scenarios related to AI cybersecurity. This section isn’t just about identifying problems but also about giving solid, usable solutions. (2) Section 3, meant for Nuclear Regulators, gets straight to the point about regulations, policy suggestions, and guidelines that are needed to lay down a robust, secure, and ethical foundation for the application of AI in nuclear operations. The focus is on making sure that everything adheres to international standards and laws while being practicable and clear-cut. (3) Section 4, aimed at Nuclear Reactor Operators and Cyber Defense Teams, offers an exhaustive exploration and technical reports, with clear recommendations and scenario analyses vital to protect operational environments and guarantee the secure application of AI in nuclear reactor operations. The goal is simple: as we step into an era where AI becomes a fundamental element of our technological and energy infrastructures, this guide is here to act as a clear, direct handbook, ensuring that AI is implemented within the nuclear sector in a manner that is secure, responsible, and practical. It’s about striking a balance – optimizing the undeniable benefits offered by AI while securing and shielding against potential cyber threats as we move through this new and complex landscape.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

ARCADE Analysis Methods & Validation Pathway

The Advanced Reactor Cyber Analysis and Development Environment (ARCADE) provides an automated analysis system which supports risk-informed performance based (RIPB) evaluations of nuclear control systems. Every possible cyber threat which could lead to consequence is identified by simulating the unsafe control action sequences which transform digital harm into physical harm. Eliminating the simulation of complex digital cyber attack chains cuts out unnecessary computational overhead and focuses directly on the physics of cyber-physical attacks. This focus enables designers to make informed decisions which can entirely eliminate categories of cyber threats against advanced reactors through the physical nature of the plant design. This narrowing of cyber threat against nuclear power plants through the physics of the system is intended to make any remaining threat management and cost efficient. This is the goal of the Tiered Cyber Analysis (TCA) outlined in NRC Draft Regulation Guide (RG) 5.96, which provides a RIPB cybersecurity approach for new reactors. ARCADE has been custom developed to meet the demands of the rigorous analysis required in Tier 1 of the TCA, which forms the foundation of the TCA process. Currently, ARCADE is still under development, but has made significant leaps in capability. A pilot analysis on the opensource Asherah simulator was performed which demonstrated key functionality goals. The next stage of ARCADE development involves improvements to the applications which support the analysis system, and enabling the analysis system to utilize the full suite of unsafe control action simulations. Since the analysis method’s core functions are complete, validation of the analysis method will be started concurrent to the next development stages. The automated analysis ARCADE will provide can radically change the cybersecurity design process for advanced reactors, reducing the cost of security implementation while enhancing cyber resilience. The pathway for ARCADE’s development to this goal has become much clearer. The majority of technical hurdles have been cleared, and the remaining development needs have been solidified. ARCADE is now capable of assisting the advanced reactor design process and directly support advanced reactor industry RIPB practices.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Protecting and Defending against Autonomous Control Systems and Digital Twin Cyber Attacks: Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Models in Nuclear Power Plants (Final)

Navigating through the complex tapestry of technological advancements, "Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Model in Nuclear Power Plants" stands at the intersection of cybersecurity and nuclear power plant operations, embarking on a journey through the intricacies of securing digital twins against malicious cyber activities. As nuclear power plants progressively integrate digital twin technology and machine learning models to optimize operations and ensure system reliability, they inadvertently expose themselves to a new spectrum of vulnerabilities, notably in the realm of hyperparameter attacks. Hyperparameters, integral in machine learning model tuning and optimal performance of digital twins, have emerged as a target for adversaries aiming to destabilize the predictive capabilities and therefore, the operational accuracy of these digital entities within critical infrastructures like nuclear plants. This paper, therefore, meticulously threads the needle through the development of a robust response strategy, poised to shield these digital reflections against calculated hyperparameter manipulations, ensuring that the digital twin can effectively and securely function as a reliable proxy for its physical counterpart. The ensuing sections delve into the orchestrated maelstrom of multi-rate time-changing intelligent coordinated hyperparameter attacks and the implementation of event-triggered predictive control, laying down a structured, predictive, and responsive framework that safeguards the nexus where the digital and physical realms of nuclear power plants coalesce. The operational integrity of digital twins in nuclear power plants depends critically on the security of machine learning hyperparameters. This study makes two different contributions. First, a decision-based idea known as a multi-rate time changing intelligent coordinated hyperparameter attack is put forth. In this attack, many hyperparameters are repeatedly changed using both random and intelligent optimal techniques by the attacker. These assaults introduce varied rates at different attack steps, compromise various amounts of hyperparameters, and improve stealth and flexibility. Second, a technique is developed for event triggered predictive control to rapidly respond to potential hyperparameter attacks. This control integrates a sliding window framework, retaining a history of previous data points and employing linear regression to predict the next data point from the current dataset. The control gain K is determined using the Lyapunov-Krasovskii method, and subsequently, an action is developed. Finally, the outcome of the simulation demonstrates the viability of the proposed method for defending nuclear power plant digital twins from hyperparameter attacks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗