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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Vulnerabilities in Artificial Intelligence and Machine Learning Applications and Data

Artificial intelligence (AI) applications driven by machine learning (ML) are transformational technologies within the international nuclear security regime. Advancements realized by AI—faster and improved data insights, more efficient and automated processes, reductions in human error—enable nuclear security applications such as behavior analysis for insider threat mitigation, source tracking of stolen nuclear material, and facial recognition software for physical protection. In addition to the advantages, however, there are also inherent vulnerabilities and threats associated with its use and risk mitigations must be built into any AI/ML-enabled systems. This work provides a background on AI and ML and different data types used in the field, including open-source intelligence information (OSINT) that is discoverable by AI tools and application data that are used by AI tools for decision-making and automation. Current and potential AI applications and vulnerabilities related to their use within the nuclear security regime are also discussed.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2019

Execution of an Alternative Modeling Strategy for Closure of the Rainier Mesa Corrective Action Unit at the Nevada National Security Site - 19099

The Underground Test Area (UGTA) Project of the U.S Department of Energy (DOE) is investigating radionuclide contaminant movements in groundwater derived from a decades-long period of underground nuclear testing at the Nevada National Security Site (NNSS) to support regulatory corrective action and closure requirements. The project is built around an integrated campaign of drilling and sampling, data interpretation, computer modeling, and broader monitoring activities conducted under a joint Federal Facilities Agreement and Consent Order (1996 as amended) (FFACO) between the State of Nevada, the DOE, and the U.S. Department of Defense. The FFACO strategy is applied to individual Corrective Action Units (CAUs) that roughly envelop disparate geographic testing areas at the NNSS. The Rainier Mesa/Shoshone Mountain (RMSM) CAU is a geologically and hydrologically complex system lying in a remote up-gradient section of the NNSS. It was the site of 67 underground tests conducted between 1957 and 1992, 61 at Rainier Mesa proper, and 6 at Shoshone Mountain, isolated to its south. The original strategy to close the RMSM CAU required a simulation-based investigation to define the evolution of contaminated volumes of groundwater (in 3D) under uncertainty as a means to define a surface-projected contaminant boundary and to serve as a basis for longer term monitoring activities. Early models developed under this strategy proved to be quite complex and difficult to calibrate. Additional costs required to better characterize the system were judged to be out of balance with the low risks associated with this particular CAU. As a result, an Alternative Modeling Strategy (AMS) was adopted. The AMS removes the requirement for developing ensembles of 3D simulations and replaces it with a simpler one requiring the generation of multiple 1D streamline models. These are meant to encompass a range of alternative, yet plausible release and transport scenarios as a means to bound the overall lateral extent of contaminant transport. This paper will review the modeling approach developed under the AMS and its application to the Rainier Mesa portion of the CAU. Preliminary model results made under different parametric assumptions and boundary condition scenarios will be presented. Additional refinements of the model and its further application will be discussed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
NASA NTRS1994

Robotic tape library system level testing at NSA: Present and planned

In the present of declining Defense budgets, increased pressure has been placed on the DOD to utilize Commercial Off the Shelf (COTS) solutions to incrementally solve a wide variety of our computer processing requirements. With the rapid growth in processing power, significant expansion of high performance networking, and the increased complexity of applications data sets, the requirement for high performance, large capacity, reliable and secure, and most of all affordable robotic tape storage libraries has greatly increased. Additionally, the migration to a heterogeneous, distributed computing environment has further complicated the problem. With today's open system compute servers approaching yesterday's supercomputer capabilities, the need for affordable, reliable secure Mass Storage Systems (MSS) has taken on an ever increasing importance to our processing center's ability to satisfy operational mission requirements. To that end, NSA has established an in-house capability to acquire, test, and evaluate COTS products. Its goal is to qualify a set of COTS MSS libraries, thereby achieving a modicum of standardization for robotic tape libraries which can satisfy our low, medium, and high performance file and volume serving requirements. In addition, NSA has established relations with other Government Agencies to complete this in-house effort and to maximize our research, testing, and evaluation work. While the preponderance of the effort is focused at the high end of the storage ladder, considerable effort will be extended this year and next at the server class or mid range storage systems.

Shields, Michael F.↗
DOE OSTI2019

Transitioning from Cobalt-60 to X-Ray or E-Beam for Medical Sterilization: Filling Data and Education Gaps

The Office of Radiological Security (ORS) within the U.S. DOE’s National Nuclear Security Administration (NNSA) is charged to “work with government, law enforcement, and businesses across the globe to protect radioactive sources used for medical, research, and commercial purposes; remove and dispose of disused radioactive sources; and reduce the global reliance on high activity radioactive sources through the promotion of viable non-radioisotopic alternative technologies.” In this effort, the NNSA/ORS, along with the Office of Defense Nuclear Nonproliferation R&D, has been working with government and private entities that utilize gamma-ray irradiators in areas that include blood irradiation, radiotherapy, biology research, well logging, and radiation processing to consider alternative technologies that do not depend upon radioactive materials. In the medical device industry, nearly 50% of products are sterilized with ethylene oxide, 40.5% with cobalt-60 gamma-rays, 4.5% with electron-beam (e-beam), and <1% with X-ray.(1,2) The cobalt-60 irradiation facilities use an estimated 150 MCi of isotope to sterilize approximately 200 million cubic feet of product annually.(3) Due to growing safety regulations governing cobalt-60 use, continued advancements in e-beam and X-ray accelerator technology, and the greater irradiation durations and costs for cobalt-60 irradiation compared to e-beam and X-ray, switching to these non-radionuclide modalities is increasingly economically competitive. However, this breakdown of 40.5% cobalt-60 and 5.5% accelerator for product sterilization illustrates the difficulty that market forces and industry players have had in overcoming barriers in transitioning to non-nuclide alternatives. Besides those associated with construction of new e-beam and X-ray facilities, significant financial and regulatory obstacles are also associated with the irradiation and comprehensive testing of product constituent polymers and elastomers. The standards associated with product material performance testing are the Association for the Advancement of Medical Instrumentation (AAMI) Technical Information Report 17 (TIR 17), and its referenced American Society for Testing and Materials (ASTM) polymer testing standards. In the study now underway, and the subject of this article, the NNSA/ORS provided R&D funding to the Pacific Northwest National Laboratory (PNNL) in partnership with university researchers, leading medical product manufacturers, accelerator manufacturers, and contract sterilizers. The resulting Team Nablo (in memory of Sam Nablo) includes individuals representing PNNL, Becton Dickinson (BD), Johnson & Johnson, Stryker, Texas A&M University (TAMU), Steri-Tek, Mevex, Ion Beam Applications (IBA), and Sterigenics. The goals of this study are to 1) Identify specific polymer and/or elastomer-based medical products that are currently sterilized with gamma-rays, and for which there are data gaps and would be of greatest industry impact, 2) Work with relevant ASTM subcommittees for guidance and for potential standard revision, 3) Work with an appropriate Food & Drug Administration (FDA) office for potential collaboration on a guidance document for device manufacturers, 4) Measure the physical effects that product materials exhibit when they are given sterilization-level radiation doses from cobalt-60, e-beam or X-ray, and determine whether these effects would preclude the use of e-beam or X-ray for sterilization, 5) Communicate the resulting scientific datasets and findings via industry conferences, peer-reviewed journals, and relevant trade magazines, and 6) Post the resulting datasets on a publicly accessible website, which can be built on in future years. The end objective is to identify technical barriers preventing the adoption of X-ray and e-beam technologies, and share the results to encourage X-ray and e-beam adoption where feasible.

sterilization, alternate technologies, gamma, X-ra↗
DOE OSTI2023

Enabling Cybersecurity, Situational Awareness and Resilience in Distribution Grids with High Penetration of Photovoltaics (CARE-PV) (Final Report)

Since legacy distribution systems have very limited visibility beyond the substation, high penetration of PV at the grid edge presents some unique operational challenges. One approach to address these challenges is to use information from advanced metering infrastructure (AMI) and µPMUs. However, exploiting this information is impacted by a number of factors, including multi-timescale measurements, volume of data generated, communication network impairments (e.g., information loss and latency) and susceptibility to cyber-attacks. Therefore, one of the critical tasks involved in the management of a distribution grid is to develop complete situational awareness by integrating cyber-security mechanisms with state estimation strategies and leveraging this situational awareness to assure energy services at strategic locations while exploiting AMI/PV inverter/ µPMU data. This CARE-PV project addresses the fundamental challenges in situational awareness and resilience to cyber and physical vectors by exploiting the synergy between innovative modeling, estimation, data analytics, testing and validation using smart PV inverters designed at K-State and facilities at NREL. Specifically, the project involved the development, testing and validation of the following novel enabling technologies: (Thrust 1) Resilience to cyber vectors that impact data integrity was addressed via a two-level defense strategy that combines cyber intrusion detection using self-learning, cooperative smart PV inverters, and a novel moving target defense framework to combat data integrity attacks. (Thrust 2) Resilience to cyber-physical vectors that impact situational awareness by limiting data availability was addressed via novel centralized and decentralized, sparsity-based static and dynamic state estimation approaches that enhance observability even when the underlying system is unobservable. (Thrust 3) Leveraging a unique probabilistic sensitivity analysis approach accompanied by one-of-a-kind dominant influencer set computation, the vulnerability of critical infrastructure at strategic locations was evaluated so that proactive PV-based control strategies can be used to support operations under normal/outage scenarios. These CARE-PV project innovations were demonstrated on both small-scale IEEE and larger utility-scale testbeds (Thrust 4). Feedback from Industry Advisory Board members was used to formulate a commercialization pathway for a subset of CARE-PV technologies. These CARE-PV technologies will ultimately lead to reliable and secure, large-scale integration of renewable energy and mitigate the risk of energy disruption resulting from cyber incidents and other emerging threats within the energy environment.

14 SOLAR ENERGY↗
DOE OSTI2024

Codes for "Shallow Soil Response to a Buried Chemical Explosion with Geophones and Distributed Acoustic Sensing" DAG - 01101646

The codes reproduce the figures of the manuscript entitled "Shallow Soil Response to a Buried Chemical Explosion with Geophones and Distributed Acoustic Sensing" submitted to Journal of Geophysical Research - Solid Earth. Geophone data and Distributed acoustic sensing (DAS) data recorded during the Phase II of the The Source Physics Experiment (SPE) along a fiber-optic cable offshore were processed to understand the response of the shallow subsurface to an explosion. This Ground-based Nuclear Detonation Detection (GNDD), Low Yield Nuclear Monitoring (LYNM), and Source Physics Experiment (SPE) research was funded by the National Nuclear Security Administration, Defense Nuclear Nonproliferation Research and Development (NNSA DNN R&D).

Viens, Loic↗
DOE OSTI2024

ARCADE (Advanced Reactor Cyber Analysis and Development Environment)

SAND2025-11780O ARCADE (Advanced Reactor Cyber Analysis and Development Environment) software performs cybersecurity experiments on Defensive Cyber Security Architectures (DCSA) for Distributed Control Systems (DCSs). The application is integrated into a cohesive environment that performs cyber risk analyses and reduces costs. ARCADE can investigate the entire cyber-attack surface of a DCS from the physics of control, down to the firmware of individual components with automated efficiency. ARCADE has five major functional components: the Data Broker system, the virtualization environment, the cyber-attack simulator, the cyber-physical analysis system, and the physics simulator. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Valme, Romuald↗
NASA NTRS2003

Envisioning a 21st Century, National, Spacecraft Servicing and Protection Infrastructure and Demand Potential: A Logical Development of the Earth Orbit Economy

The modern world is extremely dependent on thin strings of several hundred civil, military, and commercial spacecraft/satellites currently stationed in space. They provide a steady stream of commerce, defense, and knowledge data. This dependency will in all likelihood increase significantly during this century. A major disruption of any kind in these essential systems and networks could be socially, economically, and politically catastrophic, on a global scale. The development of a space-based, robotic services economy could be useful in mitigating this growing risk, from an efficiency and security standpoint. This paper attempts to suggest what makes sense to invest in next for the logical, economic development of Earth orbit i.e., after ISS completion. It expands on the results of an advanced market research and analysis study that sampled the opinions of several satellite industry executives and presents these results within a broad policy context. The concept of a spacecraft carrier that serves as the nucleus of a national, space-based or on-orbit, robotic services infrastructure is introduced as the next logical step for United States leadership in space. This is viewed as a reasonable and appropriate followon to the development of ELVs and satellites in the 1950s and 1960s, the Space Shuttle/PRLV in the 1970s and 1980s, and the International Space Station (ISS) in the 1980s, 1990s and 2000s. Large-scale experience in LEO-to-GEO spacecraft/satellite servicing and protection by robotic means is assumed to be an indispensable prerequisite or stepping-stone toward the development and preservation of the large scientific exploration facilities that are envisioned by NASA for operation beyond GEO. A balanced, return on national investment (RONI) strategy for space, focused on the provision of enhanced national/homeland security for increased protection, national economic/industrial expansion for increased revenue, and national scientific exploration for increased knowledge is recommended as the next strong, irrepressible goal toward realizing and achieving the official NASA vision and mission.

Horsham, Gary A.↗
NASA NTRS2014

Control and Non-Payload Communications Generation 1 Prototype Radio Flight Test Report

Unmanned aircraft (UA) represent a new capability that will provide a variety of services in the Government (public) and commercial (civil) aviation sectors. The growth of this potential industry has not yet been realized because of the lack of a common understanding of what is required to safely operate Unmanned Aircraft Systems in the National Airspace System (UAS in the NAS). The desire and ability to fly UA is of increasing urgency. The application of UA to perform national security, defense, scientific, and emergency management are driving the critical need for less restrictive access by UA to the NAS. Existing Federal Aviation Regulations, procedures, and technologies do not allow routine UA access to the NAS. Access to the NAS is hampered by challenges such as the lack of an onboard pilot to see and avoid other aircraft; the ability of a single pilot or operator to control multiple UA; the reliance on command and control (C2) links; the altitudes, speeds, and duration at which the aircraft fly; and the wide variation in UA size and performance. NASA is working with other Government agencies to provide solutions that reduce technical barriers and make access to the NAS routine. This goal will be accomplished through system-level integration of key concepts, technologies, or procedures and through demonstrations of these integrated capabilities in an operationally relevant environment. This project provides an opportunity to transition the acquired empirical data and knowledge to the Federal Aviation Administration and other stakeholders to help them define the requirements for routine UA access to the NAS.Radio communications channels for UA are currently managed through exceptions and use either Department of Defense frequencies for line-of-sight (LOS) and satellite-based communications links, low-power LOS links in amateur bands, or unlicensed Industrial/Scientific/Medical (ISM) frequencies. None of these frequency bands are designated for safety and regularity of flight. Only recently has radiofrequency (RF) spectrum been allocated by the International Telecommunications Union specifically for commercial UA C2, LOS communication (L-Band: 960 to 1164 MHz, and C-Band: 5030 to 5091 MHz). The safe and efficient integration of UA into the NAS requires the use of protected RF spectrum allocations and a new data communications system that is both secure and scalable to accommodate the potential growth of these new aircraft. Data communications for UA-referred to as control and non-payload communications (CNPC)-will be used to exchange information between a UA and a ground station (GS) to ensure safe, reliable, and effective UA flight operation. The focus of this effort is on validating and allocating new RF spectrum and data link communications to enable civil UA integration into the NAS. Through a cost-sharing cooperative agreement with Rockwell Collins, Inc., the NASA Glenn Research Center is exploring and performing the necessary development steps to realize a prototype UA CNPC system. These activities include investigating signal waveforms and access techniques, developing representative CNPC radio hardware, and executing relevant testing and validation activities. There is no intent to manufacture the CNPC end product, rather the goals are to study, demonstrate, and validate a typical CNPC system that will allow safe and efficient communications within the L-Band and C-Band spectrum allocations. The system is addressing initial "seed" requirements from RTCA, Inc., Special Committee 203 (SC-203) and is on a path to Federal Aviation Administration certification. This report provides results from the flight testing campaign of the Rockwell Collins Generation 1 prototype radio, referred hereafter as the "radio." The radio sets operate within the 960- to 977-MHz frequency band with both air and ground radios using identical hardware. Flight tests involved one aircraft and one GS. Results include discussion of aircraft flight paths and associated radio performance.

Shalkhauser, Kurt A.↗
DOE OSTI2016

Ceramic Seal.

Containment/Surveillance (C/S) measures are critical to any verification regime in order to maintain Continuity of Knowledge (CoK). The Ceramic Seal project is research into the next generation technologies to advance C/S, in particular improving security and efficiency. The Ceramic Seal is a small form factor loop seal with improved tamper-indication including a frangible seal body, tamper planes, external coatings, and electronic monitoring of the seal body integrity. It improves efficiency through a self-securing wire and in-situ verification with a handheld reader. Sandia National Laboratories (SNL) and Savannah River National Laboratory (SRNL), under sponsorship from the U.S. National Nuclear Security Administration (NNSA) Office of Defense Nuclear Nonproliferation Research and Development (DNN R&D), have previously designed and have now fabricated and tested Ceramic Seals. Tests have occurred at both SNL and SRNL, with different types of tests occurring at each facility. This interim report will describe the Ceramic Seal prototype, the design and development of a handheld standalone reader and an interface to a data acquisition system, fabrication of the seals, and results of initial testing.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS2011

Land and Atmosphere Near-Real-Time Capability for Earth Observing System

The past decade has seen a rapid increase in availability and usage of near-real-time data from satellite sensors. The EOSDIS (Earth Observing System Data and Information System) was not originally designed to provide data with sufficiently low latency to satisfy the requirements for near-real-time users. The EOS (Earth Observing System) instruments aboard the Terra, Aqua and Aura satellites make global measurements daily, which are processed into higher-level 'standard' products within 8-40 hours of observation and then made available to users, primarily earth science researchers. However, applications users, operational agencies, and even researchers desire EOS products in near-real-time to support research and applications, including numerical weather and climate prediction and forecasting, monitoring of natural hazards, ecological/invasive species, agriculture, air quality, disaster relief and homeland security. These users often need data much sooner than routine science processing allows, usually within 3 hours, and are willing to trade science product quality for timely access. While Direct Broadcast provides more timely access to data, it does not provide global coverage. In 2002, a joint initiative between NASA (National Aeronautics and Space Administration), NOAA (National Oceanic and Atmospheric Administration), and the DOD (Department of Defense) was undertaken to provide data from EOS instruments in near-real-time. The NRTPE (Near Real Time Processing Effort) provided products within 3 hours of observation on a best-effort basis. As the popularity of these near-real-time products and applications grew, multiple near-real-time systems began to spring up such as the Rapid Response System. In recognizing the dependence of customers on this data and the need for highly reliable and timely data access, NASA's Earth Science Division sponsored the Earth Science Data and Information System Project (ESDIS)-led development of a new near-real-time system called LANCE (Land, Atmosphere Near-Real-Time Capability for EOS) in 2009. LANCE consists of special processing elements, co-located with selected EOSDIS data centers and processing facilities. A primary goal of LANCE is to bring multiple near-real-time systems under one umbrella, offering commonality in data access, quality control, and latency. LANCE now processes and distributes data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Atmospheric Infrared Sounder (AIRS), Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E), Microwave Limb Sounder (MLS) and Ozone Monitoring Instrument (OMI) instruments within 3 hours of satellite observation. The Rapid Response System and the Fire Information for Resource Management System (FIRMS) capabilities will be incorporated into LANCE in 2011. LANCE maintains a central website to facilitate easy access to data and user services. LANCE products are extensively tested and compared with science products before being made available to users. Each element also plans to implement redundant network, power and server infrastructure to ensure high availability of data and services. Through the user registration system, users are informed of any data outages and when new products or services will be available for access. Building on a significant investment by NASA in developing science algorithms and products, LANCE creates products that have a demonstrated utility for applications requiring near-real-time data. From lower level data products such as calibrated geolocated radiances to higher-level products such as sea ice extent, snow cover, and cloud cover, users have integrated LANCE data into forecast models and decision support systems. The table above shows the current near-real-time product categories by instrument. The ESDIS Project continues to improve the LANCE system and use the experience gained through practice to seek adjustments to improve the quality and performance of the system. For example, anGC-compliant Web Map Service (WMS) will be added shortly that will allow users to download geo-referenced MODIS images for arbitrary bounding boxes. Further, an OGC-compliant Web Coverage Service (WCS) will be added later this year that will expedite user access to arbitrary data subsets or re-formatted products. AIRS images are now served through WMS and available in multiple formats (PNG, GeoTIFF, KMZ). NASA has established a LANCE User Working Group to steer the development of the system and create a forum for sharing ideas and experiences that are expected to further improve the LANCE capabilities. The LANCE system has proved a success by satisfying the growing needs of the applications and operational communities for land and atmosphere data in near-real-time. NASA's Earth Sciences Division was able to leverage existing science research capabilities to provide the near-real-time community with products and imagery that support monitoring of disasters in a timely manner.

Murphy, Kevin J.↗
DOE OSTI2024

Nonparametric Multiparticle Set Methods for Interpreting Environmental Samples

Collection and analysis of environmental samples is commonly used by a range of stakeholders in nuclear safeguards and security contexts. While the ubiquity of samples and their transport in the environment allow regular collection, developing and demonstrating methods for analyzing these samples is difficult. In this work, an environmental sample consists of a set of one or more individual particles. Recent advances in reactor simulation have allowed us to generate data that are more representative of real-world environmental samples, enabling statistically defensible method development and testing. The most notable of these advances is a drastic increase in the number of material depletion regions, which allows our simulations to capture the variation in isotopic composition seen at length scales consistent with environmental samples. Traditional approaches for handling multiparticle samples treat each particle in the sample individually, estimating the quantity of interest (e.g., core-average burnup) resulting from measurement and analysis of signatures (e.g., nuclide assays) from each individual particle. Individual estimates are then averaged to generate a single estimate of the quantity of interest over the entire sample. In this presentation, we introduce two novel approaches for interpreting environmental samples that comprise of multiple particles: (1) the Quantile-Quantile Comparator, which uses a multivariate generalization of quantile-quantile plots for comparing unknown statistical distributions, and (2) the Set Transformer, an attention-based neural network module designed to model interactions among elements (particles) in the input set (sample). Statistically representative sampling cannot be guaranteed as samples are passively collected and are beholden to what particles are available in the environment. These new analysis methods for set-input problems are expected to be more robust than traditional approaches to issues of sampling bias where particles are not uniformly distributed throughout regions of interest, as well as generally outperform traditional approaches by jointly considering all elements in the set. We will present results comparing the performance of traditional single particle approaches and the novel Quantile-Quantile Comparator and Set Transformer for interpretation of simulated environmental samples.

Phathanapirom, Birdy↗
DOE OSTI2021

Evaluating Probability of Containment Effectiveness at a GCS Sites using integrated assessment modeling approach with Bayesian decision Networks

Improved scientific and engineering understanding of the behavior of geologic CO2 storage together with established regulatory framework and incentive structures raise the prospects for accelerated, large-scale deployment of this greenhouse gas emissions reduction approach. Incentive structures call for the establishment of appropriate verification and accounting approaches to support claims of the integrity of a geologic storage complex and to justify taking credit for long-term storage. In this study, we present a framework for assessing the probability of containment effectiveness over the lifetime of a geologic carbon storage site (e.g., after 70 years of injection and post-injection site performance) using forward stochastic model realizations based on site characterization data and using a monitoring-informed Bayesian network based on hypothetical detectability from surface seismic surveys over the site injection and post-injection phases. The National Risk Assessment Partnership’s open-source Integrated Assessment Model (NRAP-Open-IAM) was utilized to develop an ensemble of 10,000 a priori stochastic forecasts of CO2 containment. Those simulations were used to train the Bayesian network model to estimate the prior probabilities of the CO2 leakage mass into overlying, monitorable aquifers considering the uncertainties in the reservoir properties, permeability of potentially leaky wells and the overlying aquifers. The conditional probabilities in the Bayesian network were either learned from the NRAP-Open-IAM simulations or derived from the predefined detection thresholds for the monitoring method. Observations obtained from monitoring, over time during the site operation phases were then used to generate updated posterior probabilities of containment (and any loss from containment) in the Bayesian network by propagating the prior probabilities through the conditional probabilities. We demonstrate how to construct and use the Bayesian network for verifying the long-term storage complex effectiveness informed by monitoring based on the NRAP-Open-IAM simulations previously developed for the FutureGen 2.0 site. This approach may have relevance for stake holders to demonstrate secure geologic storage, provide a defensible, probabilistic approach to claim credit for geologic storage, and to estimate the likelihood that any fraction of the claimed credit may need to be refunded to the creditor based on available monitoring information.

Bayesian network, Risk assessment, Monitoring, car↗
DOE OSTI2019

Rare earth elements of fly ash from Wyoming's Powder River Basin coal

The advancement and increasing interests in green energy production and environmental protection technologies have spurred the demand for rare earth elements. On the other hand, the U.S. has to rely 100% on import of these materials as the industry was crashed because of the environmental issues associated with mining and processing, and more importantly lack competitiveness. As rare earth elements have many applications in defense and national security sectors, some of the elements were listed as strategic materials or critical materials because of the uncertainties in supply and prices. Therefore, it has become imperative in searching for alternative resources for supplying rare earth elements including unconventional resources. With the strong incentive, coal fly ash is identified as one of the candidates among them. In this study, we present rare earth element data for the 42 ash samples (all derived from Powder River Basin coal) that we collected from seven states (UT, WY, IO, WI, ND, CO, and MI) representing 158 million tons of fly ash. Overall, the results indicate scattered distributions of rare earth elements with concentration ranging from 156 to 590 ppm although all the ash samples originated from the same coal basin (Powder River Basin). The rare earth element resource in these ashes is estimated between 74000 and 106000 t. The ash samples were also characterized by elemental analysis, XRD, SEM-EDS and BET surface area. The characterizations of the ashes were discussed as they might have implications in the subsequent rare earth element extraction processes. In summary, fly ash may represent a potential resource for rare earth element production.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Method for Assessment of Security-Relevant Settings in Anomaly-Based Intrusion Detection for Industrial Control Systems

Ensuring the integrity of Ethernet-based networks is a challenging and constantly evolving domain. This problem is exacerbated for those operational technology (OT) networks supporting industrial control systems (ICS) since much of that equipment was originally designed to be on a network that was isolated and generally considered free of malefactors. Increasing pressure to bridge these systems with traditional information technology (IT) networks has introduced a bevy of new threats. In response, both academia and industry have responded with security solutions tailored to ICS environments. Deploying these protection systems often involves several configuration choices. While some of these choices are clear (e.g., block/enable protocol X) others are far more subjective (e.g. alert threshold == 3.43). Further complicating the situation, while often similar to IT networks, OT networks have unique challenges and characteristics that make the task of protecting them simultaneously more difficult and straight forward.Extant solutions for quantifying the relative security of intrusion detection systems fail to effectively support the operators of said systems with understanding the impact of various configuration changes. Further, they assume that the attacks are static and not subject to manipulation or alteration in the face of defenses. In this paper, we present a threat-based method for quantifying the relative impact of various security settings for intrusion detection systems (IDSs) within ICS environments. This method provides operational staff with a clear understanding of the relative impact of their settings and assumes that the attacks levied against them are dynamic. The model is described in detail, we apply the model to a synthetic data set, and discuss the inferences that can be made and what types of decisions they could be used to support.

Gillen, Rob↗
DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

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