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TITANS CCD Open House Poster presenting summer intern work completed on an automated patch diffing pipeline, targeted for reverse engineers and vulnerability researchers.
Nuclear power plant (NPP) cybersecurity research often relies on hardware-in-the-loop (HIL) testbeds that integrate real hardware components into simulated environments. These testbeds allow researchers to identify vulnerabilities, evaluate attack impacts, and test security measures in a controlled setting. Furthermore, previous HIL testbeds lacked fidelity to accurately represent real nuclear systems, limiting the scope of cybersecurity analysis. This study presents the creation of a HIL testbed, devised upon a full-scope, high-fidelity NPP simulator, to facilitate realistic and comprehensive cybersecurity research. To demonstrate its capabilities, the control logic for the steam generator water level was migrated from the simulator to an external programmable logic controller. As a practical application of the developed testbed, supply chain attack scenarios were simulated by injecting malicious code into the controller logic, and the effects of manipulating sensor inputs and control commands were observed. While this HIL testbed provides more detailed simulations, enhanced realism, and wider applicability compared to other options utilizing a less complex simulator, it is also more intricate and costly. For this reason, we include a detailed comparison with some alternative architectures to aid fellow researchers and practitioners in the selection of a suitable HIL architecture based on specific research objectives.
Tropical peatlands are among the most carbon-dense terrestrial ecosystems yet recorded. Collectively, they comprise a large but highly uncertain reservoir of the global carbon cycle, with wide-ranging estimates of their global area (441 025–1700 000 km 2 ) and below-ground carbon storage (105–288 Pg C). Substantial gaps remain in our understanding of peatland distribution in some key regions, including most of tropical South America. Here we compile 2413 ground reference points in and around Amazonian peatlands and use them alongside a stack of remote sensing products in a random forest model to generate the first field-data-driven model of peatland distribution across the Amazon basin. Our model predicts a total Amazonian peatland extent of 251 015 km 2 (95th percentile confidence interval: 128 671–373 359), greater than that of the Congo basin, but around 30% smaller than a recent model-derived estimate of peatland area across Amazonia. The model performs relatively well against point observations but spatial gaps in the ground reference dataset mean that model uncertainty remains high, particularly in parts of Brazil and Bolivia. For example, we predict significant peatland areas in northern Peru with relatively high confidence, while peatland areas in the Rio Negro basin and adjacent south-western Orinoco basin which have previously been predicted to hold Campinarana or white sand forests, are predicted with greater uncertainty. Similarly, we predict large areas of peatlands in Bolivia, surprisingly given the strong climatic seasonality found over most of the country. Very little field data exists with which to quantitatively assess the accuracy of our map in these regions. Data gaps such as these should be a high priority for new field sampling. This new map can facilitate future research into the vulnerability of peatlands to climate change and anthropogenic impacts, which is likely to vary spatially across the Amazon basin.
This presentation discusses the rise in digital energy infrastructure and associated threats. It covers an in-depth risk assessment of digital energy equipment and provides real world examples of threats, vulnerabilities, and known incidents. Research conducted by INL assessing and addressing these risks follows, concluding with a dive into Cyber-Informed Engineering (CIE) and it's application to digital energy infrastructure, using battery energy storage systems (BESS) as an example.
Extreme weather events related to climate change, and an aging electricity infrastructure are disrupting reliable electricity services to a greater degree. Further, previous research has found that more socially vulnerable populations are more likely to live in areas with a higher probability of power outages. Here, this study examines the issues that people face during power outages and the resources that help individuals maintain resilience during power outages caused by extreme weather events in socially vulnerable communities. Using qualitative data from focus groups with 56 individuals in Central and North Florida, the research highlights lived experiences during outages and difficulties using and accessing resources during these conditions. Based on a qualitative review of the focus group discussions, this paper explores the solutions and support systems residents believe would improve their ability to cope. The findings offer insights to guide policy and strategic planning, with the goal of strengthening personal preparedness and response by focusing on the resources people consider most helpful for enduring frequent and severe outages.
The goal of this research program was to build a next generation integrated suite of science-driven modeling and analytic capabilities, and a more expanded and connected community of practice, for analyses of the stressors, impacts, adaptations and vulnerabilities of global and regional change. The emphasis was on understanding energy-water-land interactions and feedbacks and interdependent infrastructures at appropriate regional and temporal scales. Although the scope spans many complex facets of data, modeling, and analysis, as well as scales appropriate for integrated impacts and adaptation research, the focus of this effort was the development of multi-model, multi-scale capabilities spanning the domains of Multi-Sector Dynamics (MSD) models; Impact, Adaptation, and Vulnerability (IAV) models; and Earth System Models (ESMs).
Apps that run on mobile devices are one of critical components of the electric vehicle (EV) ecosystem and pose possible threat actor points of entry that may impact the trust and security of EV charging systems in the future. Mobile apps often rely on communication between cloud servers and users, thereby creating potential points of entry for cyberattacks. Although app stores such as Apple App Store or Google Play Store generally test the security of apps, the cyber aspects may not be sufficient for many entities including DOD, federal fleets, and commercial entities. A more thorough inspection and the ability to influence developers is imminently needed. This research studies security attributes and vulnerabilities of a sample of mobile applications that support key user functions in the EV ecosystem. The study shows that all analyzed apps have security risks, categorized as either high or medium or both and a comprehensive cybersecurity guideline for developing mobile apps is necessary.
Industrial control systems prove to be vital to the health and security of the nation in our critical infrastructure. Critical infrastructure includes the most foundational systems to support modern civilization which includes water and wastewater systems, communications, and the electricity we use to name a few sectors. However, these devices' overall composition remains largely unknown and are untested from a cyber security perspective. As part of the Cyber Testing for Resilient Industrial Control Systems (CyTRICS) program, I analyzed one such energy infrastructure device to better understand how it functions, what hardware and software components are present within it, and assess it for security vulnerabilities. To achieve this, I reverse engineered binary files using Ghidra to understand system functionality and learned more about how to collaborate with other researchers on a shared Ghidra project. I learned more about how web sockets function and how to interact with them through Python to test if they are secure or not. This work led me to assess possible vulnerabilities in this device and provide a better understanding of its composition and function, which are essential to INL's mission of securing our nation's energy infrastructure.
This study supports the Office of Radiological Security’s (ORS) mission of eliminating cesium irradiators by analyzing the supply chain for self-shielded X-ray irradiators (SSXIs), identifying potential risks, and proposing mitigation measures. The research focuses on the primary components of SSXIs, including X-ray tubes, controllers, generators, and coolers or chillers, and evaluates their vulnerabilities using a comprehensive risk matrix framework. The methodology includes subject matter expert (SME) interviews with relevant manufacturers and major stakeholders, a deep literature review, and a meta-analysis of maintenance reports provided by SSXI end users. Results show that while the SSXI market is small, it’s growing, and the highly global nature of the supply chain may create vulnerabilities for critical SSXI components (X-ray tubes are the most vulnerable, followed by generators and controllers). This research communicates necessary information to address concerns of current and future end users, especially those interested in transitioning away from radioactive sources, and informs future policy aimed at supporting the irradiation industry.
Sandia’s Electric Grid Security program advances a national vision of energy dominance and accessibility, while applying our national security -emphasis on ensuring of a secure, resilient, and affordable electric system for all users. Our achievements reflect a strategic approach combining technology development; modeling, simulation, and data analytics; and partnered demonstrations and outreach to further the adoption of advanced grid and storage technologies. Our FY25 efforts leverage the strengths of our partnerships—spanning Sandia’s core science and technology competencies as well as external technology leaders—to develop the solutions today which enable the grid of tomorrow. Key accomplishments in this report that support our strategy span our technical program areas and include: • New open-source analytical tools for systems -level planning and optimization, including significant advances to the QuESt analytical environment; • Further advancement of artificial intelligence and machine learning to enhanced grid operations and planning as we rise to the challenge of new large loads; • Development of solid-state power conversion technologies and a new medium-voltage research lab; • New technologies to assess wildfire vulnerabilities and mitigate potential impacts; • Advanced applications of new cybersecurity technologies with industry partners; • Contributions to understanding the impacts of electromagnetic pulses and geomagnetic disturbances on grid components; and • Digital twin development for hybrid microgrids with multiple generators, storage, and loads. This report indicates key areas of research and engagement and summarizes the impact of Sandia’s contributions through notable accomplishments, journal publications, patents, and technical conferences and presentations. It is provided with the hope that readers discover ways we can further team to create our modern grid and apply the outcomes of our efforts. The bulk of work described herein is funded by several offices within the U.S. Department of Energy (USDOE), including the Office of Electricity (OE); Cybersecurity, Energy Security, and Emergency Response (CESER); former offices such as the Office of Energy Efficiency and Renewable Energy (EERE), the Grid Deployment Office (GDO), the Office of Clean Energy Demonstrations (OCED), and other key programs at USDOE. As we continue to state in these annual reports, the contributors to our successes are too numerous to name here, though our team wishes to express our deep gratitude to the numerous program and project sponsors at the US Department of Energy, who often function equally as technical collaborators; our many partners in industry, academia, utilities, and other national labs; and fellow researchers and business partners at Sandia whose leadership and creativity have enabled the accomplishments described herein.
Sandia’s Electric Grid Security program advances a national vision of a secure, resilient, and affordable electric system for all users. Our achievements reflect a strategic approach combining technology development; modeling, simulation, and data analytics; and partnered demonstrations and outreach to further the adoption of advanced grid and storage technologies. Our FY24 efforts leverage the strengths of our partnerships—spanning Sandia’s core science and technology competencies as well as external technology leaders—to develop the solutions today which enable the grid of tomorrow. Key accomplishments in this report that support our strategy span our technical program areas and include: • The advancement of energy storage technologies, including creation of a national Long Duration Energy Storage Consortium; • Applications of artificial intelligence and machine learning to enhanced grid operations and planning; • Development of solid-state power conversion technologies and a new medium-voltage research lab; • New technologies to assess wildfire vulnerabilities and mitigate potential impacts; • Advanced applications of new cybersecurity technologies with industry partners; • Contributions to understanding the impacts of electromagnetic pulses and geomagnetic disturbances on grid components; and • Digital twin development for hybrid microgrids with multiple generators, storage, and loads.
The rapid expansion of photovoltaic (PV) systems, particularly inverters, has introduced new cybersecurity challenges that threaten both local operations as well as the broader electrical grid’s stability. PV inverters, integrated into critical energy infrastructure are potential targets for cyber attacks due to vulnerabilities in firmware, remote access systems, and communication protocols. The Coordinated Vulnerability Disclosure (CVD) process, as defined by the Cybersecurity and Infrastructure Security Agency (CISA), provides a framework for identifying, reporting, and addressing these vulnerabilities in a transparent and collaborative manner. This report outlines the CVD process as it applies to PV systems, detailing the roles of key stakeholders, such as manufacturers, grid operators, and security researchers. The report also highlights specific challenges in managing vulnerabilities for new and legacy PV systems, which includes those introduced by insecure communications and third-party supply chain components. By adhering to the CVD process, the PV industry can mitigate cybersecurity risks, ensure regulatory compliance, and maintain consumer trust, while safeguarding the operational resilience of the energy grid. Ultimately, the effective coordination of vulnerability management is crucial for securing the future of PV systems within the critical electric grid infrastructure landscape.
This research article presents a comprehensive review of resilience in power transmission and distribution systems, focusing on key studies that examine structural integrity, mechanical stress distribution, and system reliability under various conditions. The article synthesizes findings across multiple studies, covering assumptions, system topologies, methodologies, and challenges that affect resilience in these networks. Particular attention is given to identifying studies that address the influence of adjacent spans and tower--line interactions, as these factors play a crucial role in the stress distribution and structural dynamics within poles, towers, and transmission lines. In addition to identifying critical factors contributing to system vulnerability, this review emphasizes the importance of continued research to better understand and mitigate risks from extreme events, such as high winds combined with ice storms. By consolidating knowledge on current challenges and future needs, this work outlines future directions to advance resilience studies and contributes to ongoing efforts to strengthen power transmission and distribution networks, reducing disruptions in electricity supply and improving overall system resilience in an evolving climate landscape.
The U.S. is facing a significant increase in energy demand, driven by AI advancements, the rapid expansion of data centers, manufacturing and industrial growth, and the electrification of transportation and buildings. Buildings alone account for approximately 75% of U.S. electricity consumption and 40% of total energy use. To address these challenges, NLR leverages its state-of-the-art research facilities, advanced energy modeling, hardware-in-the-loop emulation, and real-world demonstrations to provide data-driven insights that de-risk emerging energy solutions, increase efficiency and demand flexibility, optimize grid controls, and identify vulnerabilities to enhance energy security. This presentation will highlight our research ecosystem and its role in supporting a more reliable, affordable, and adaptive energy infrastructure in the face of accelerating demand.
Existing research primarily uses census data to identify the vulnerability of communities to hazards. These vulnerability indices provide aggregated data and are not hazard-specific nor well-validated with post-event data. In contrast, our study uses household survey data (n=1065) to understand which Texan households suffered the greatest loss of their capabilities due to power outages and other utility service disruptions during Winter Storm Uri. Inspired by the Capabilities Approach, our measures of burden include the number of household capability types disrupted during the outages (e.g., cooking, heating, refrigeration), the severity of impact for each disrupted capability, and the additional time and financial costs of coping with these disruptions. We perform a clustering analysis, and find two distinct groups in our data, consisting of ‘lesser burden' and ‘heavier burden' households. Results indicate that the households experiencing the heaviest capabilities burden were most likely to experience longer power outages and the loss of water services. They were also more likely to have a Hispanic-Latino household member, lack access to a generator, live in a rented home, have larger households with more young children, fewer adults over 65, lower household incomes, been impacted by the COVID-19 pandemic, and more family characteristics that made life harder. We also fit a logistic regression model to assess the role of outage, household, and community characteristics in predicting differences in capabilities burden. Our results offer insights into enumerating the consequences of utility service disruptions on households, which can inform more targeted and equitable resilience strategies.
This study aims to enhance the security of radioactive materials during transport by analyzing commonalities in cargo thefts conducted by non-state groups such as thieves and terrorists. This research focuses on identifying patterns and trends in the methods used to steal high-value cargo, with the goal of applying these insights to improve transport security of radioactive materials. Key questions addressed include the frequency of specific tools, techniques, and insider involvement in thefts, as well as the use of weapons, electronic jamming equipment, and specialized tools. Findings will inform security design improvements and industry practices to mitigate vulnerabilities. The study involves a comprehensive review of literature and case studies, utilizing data sources from 2018 to 2023. Articles will be selected based on their relevance to thefts of valuable cargo in transit, with a focus on incidents involving non-state actors. The methodology will include statistical and inferential analysis to identify trends, with results visualized through pie charts, frequency analyses, and terrain maps. The discussion will highlight the implications of findings and provide actionable recommendations for strengthening security measures. Limitations such as data availability and reporting inconsistencies will be acknowledged. Suggestions for future improvements will be constructed using existing case studies and expert feedback. The study’s outcomes aim to raise awareness within the industry, inform policy decisions, and enhance security protocols for radioactive material transport. Metrics for impact include the potential publication of findings, presentations at conferences to raise awareness, and the subsequent actions taken by stakeholders based on the research. By identifying trends and vulnerabilities and suggesting improvements, this research contributes to preventing the illicit use of nuclear and radiological materials.
Heat exposure has been linked to psychosocial stress, an established antecedent of perinatal depression; however, evidence on heat-related stress during pregnancy in sub-Saharan Africa remains limited. We analyzed psychosocial stress scores and covariate data from the Ghana Randomized Air Pollution and Health Study, linking daily maximum and minimum shaded wet bulb globe temperature (WBGT) metrics to participants’ stress scores derived from the Crisis in Family Systems-Revised Life Events Questionnaire. We evaluated associations using ordinal logistic regression of pregnancy-average and trimester-average exposures and distributed lag non-linear models (DLNMs) to assess time-varying associations across gestation. Higher average maximum WBGT exposure across pregnancy was associated with increased odds of higher psychosocial stress; each 1 °C increase in maximum WBGT was associated with 64% higher odds of belonging to a higher stress category (OR = 1.64; 95% CI = 1.17–2.31; p = 0.0040). In trimester-average models, higher first-trimester maximum WBGT was also associated with higher stress (OR = 1.44; 95% CI = 1.15–1.81; p = 0.0014). DLNMs suggested that relatively cooler daily maximum WBGT values (25th percentile) were associated with decreased odds of stress in early pregnancy, whereas extreme daily maximum WBGT values (99th percentile) showed a pattern consistent with increased odds of stress in mid-to-late gestation. These findings highlight gestational windows in which heat exposure may influence stress, emphasizing the need for further research into underlying mechanisms and effective interventions to protect maternal mental health in heat-vulnerable settings.
The United States demand for Neodymium-Iron-Boron (NdFeB) magnets, produced from rare earth elements (REEs) such as (Nd) and Dysprosium (Dy), far exceeds its nascent domestic production capacity, rendering it reliant on vulnerable global supply chains dominated by China. To guide research and development investments in securing U.S. REE supply, defensible benchmark metrics across environmental, economic, and social dimensions are needed. In this study, we built globally-representative, process-based cradle-to-cradle life cycle inventories for Nd and Dy in NdFeB magnets lifecycles, encompassing primary material acquisition, beneficiation, smelting and refining, metal processing, specialty alloy and chemical transformation, subcomponent manufacturing, consumer application (use phase) and end-of-life management. We carried out detailed literature review, and applied process engineering principles to build industry-representative upscaled life cycle inventories for both metals. We used these models to conduct bottom-up literature review and gap analysis on existing literature, compilation of data sources for each life cycle stage (and transformations where necessary), and a preliminary technoeconomic analysis (TEA)/life cycle costing analysis (LCCA). Findings from this work emphasize the need for metal specific, representative REE LCIs to establish robust benchmarks for advancing sustainable REE technologies and guiding R&D in REE supply chains.