Search NASA⌕ Search

SEARCH · Search NASA

Results for “Human Operator”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Uptake and speciation of trace metal inputs to Wetland Soils from Illinois and South Carolina and Stream Sediments from Tennessee

Metals occur in all ecosystems, although their concentrations vary depending on their natural geologic conditions and surrounding human activities. In addition to metals intrinsically present from the geology of a particular system, metals can enter environmental systems from a wide variety of natural and anthropogenic sources, such as sediment re-suspension, mining operations, industrial processes, agricultural activities, and atmospheric deposition. While metals can be toxic at high concentrations, some metals serve as essential micronutrients for biogeochemical processes. Metal transport and availability in engineered and natural water systems depend on processes of adsorption/desorption, oxidation/reduction, dissolution/precipitation, and ligand complexation. Insights into the speciation of metals and their bioavailability will also help advance understanding of the roles of metals in the biogeochemical cycling of nutrients. We conducted batch experiments under anoxic conditions on soils and sediments collected from three different natural aquatic systems to understand their response to influxes of dissolved Cu, Ni and Zn. While soils and sediments from all sites could strongly bind added trace metals, there were substantial differences in trace metal uptake trends between different sites, especially for Cu. There was no distinct correlation between trace metal uptake and the total organic matter, iron, and sulfur content present in the samples. X-ray absorption spectroscopy indicated that the speciation of the freshly added metals taken up by the solids differs substantially from the speciation of the metals originally present in unamended samples. Cu sulfides dominated speciation at low loadings (1 µmol/g), whereas complexation to thiol groups and formation of metallic Cu governed speciation at high loadings (10 µmol/g). For Ni and Zn, adsorption to mineral surfaces and organic matter governed their speciation in materials from most sites. This study suggests that the background speciation of metals in natural aquatic systems is a poor predictor of the speciation and lability of metals introduced to terrestrial aquatic systems from anthropogenic or natural processes. Our findings imply that geochemical processes controlling trace metal speciation may vary considerably with metal loading in different natural systems. Data are provided for marsh wetland soils at Argonne National Laboratory (Marsh 1 and Marsh 2), riparian wetland soils (Riparian 1 and Riparian 2) in the Tims Branch watershed at Savannah River National Laboratory, and stream bed sediments (Stream 1 and Stream 2) from East Fork Poplar Creek near Oak Ridge National Laboratory. Data package includes the results of trace metal uptake experiments conducted on the selected sites for determining their capacity to immobilize metals under different loadings. XANES spectra for Cu, Ni, ad Zn at different loadings are included in the package. The abundance of different trace metal species obtained using linear combination fitting in ATHENA are also included in the data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

LDRD23-0730: Invoking Multilayer Networks to Develop a Paradigm for Security Science—Summary Report

Current approaches to securing high consequence facilities (HCF) and critical assets are linear and static and therefore struggle to adapt to emerging threats (e.g., unmanned aerial systems) and changing environmental conditions (e.g., decreasing operational control). The pace of change in technological, organizational, societal, and political dynamics necessitates a move toward codifying underlying scientific principles to better characterize the rich interactions observed between HCF security technology, infrastructure, digital assets, and human or organizational components. The promising results of Laboratory Directed Research and Development (LDRD) 20-0373—“Developing a Resilient, Adaptive, and Systematic Paradigm for Security Analysis”—suggest that when compared to traditional security analysis, invoking multilayer network (MLN) modeling for HCF security system components captures unexpected failure cases and unanticipated interactions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The Zwicky Transient Facility Bright Transient Survey. III. BTSbot: Automated Identification and Follow-up of Bright Transients with Deep Learning

Abstract The Bright Transient Survey (BTS) aims to obtain a classification spectrum for all bright ( m peak ≤ 18.5 mag) extragalactic transients found in the Zwicky Transient Facility (ZTF) public survey. BTS critically relies on visual inspection (“scanning”) to select targets for spectroscopic follow-up, which, while effective, has required a significant time investment over the past ∼5 yr of ZTF operations. We present BTSbot , a multimodal convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. BTSbot is able to eliminate the need for daily human scanning by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. BTSbot recovers all bright transients in our test split and performs on par with scanners in terms of identification speed (on average, ∼1 hr quicker than scanners). We also find that BTSbot is not significantly impacted by any data shift by comparing performance across a concealed test split and a sample of very recent BTS candidates. BTSbot has been integrated into Fritz and Kowalski , ZTF’s first-party marshal and alert broker, and now sends automatic spectroscopic follow-up requests for the new transients it identifies. Between 2023 December and 2024 May, BTSbot selected 609 sources in real time, 96% of which were real extragalactic transients. With BTSbot and other automation tools, the BTS workflow has produced the first fully automatic end-to-end discovery and classification of a transient, representing a significant reduction in the human time needed to scan.

Rehemtulla, Nabeel (ORCID:0000000256832389)↗
DOE OSTI2025

Quantitative Risk Assessment for Fuel Cell Electric Bus Hydrogen Storage and Refueling Facility

It is necessary to understand the safety implications and risk mitigation options for fuel cell electric bus fleet deployment, especially for related facilities responsible for operations such as production, storage, compression, and dispensing of hydrogen for use by the buses. In this report, we present a quantitative risk assessment for a potential fuel cell electric bus fleet that was motivated by efforts to improve resilience at the Portland International Airport but can be applicable to a range of hydrogen case studies and use cases. We estimated risk for a facility that produces, stores, compresses, and dispenses hydrogen for the fleet of buses, with a focus on individual risk to people in terms of annual frequency of fatality. We considered the frequency of hydrogen leaks that could result in harmful physical outcomes like jet fires or explosions, and the consequences of those outcomes for people. We created customized fault trees to calculate the frequencies of different sizes of leaks and event sequence diagrams to calculate ignition probabilities for the various leak sizes. We also leveraged the HyRAM+ toolkit to use these inputs to calculate overall risk for the facility, which we separated into one section responsible for producing, storing, and compressing hydrogen, and one section responsible for dispensing the hydrogen to the buses. We found that the dispensing area seemed to have a higher risk than the production/storage/compression area of the facility, largely because of the inclusion of a component with a high leak frequency (the heat exchanger used to cool the hydrogen before entering the vehicle, to prevent overheating and expansion of hydrogen in the onboard tank). For the example production and refueling facility we evaluated and the data we used for the analysis, the leak frequency had a larger impact on the risk differences between the two sections on the facility, compared to the physical outcome consequence, which was slightly different due to the varying fuel conditions, but not substantially different. Actions can be taken to prevent these hazards (e.g., lowering leak frequencies in system components) or to mitigate the consequences if they do occur (e.g., installing barriers to protect people if ignition events occur). The choice of which actions to take depends not only on safety considerations but also on space, time, staffing, feasibility, and financial constraints. Therefore, the quantitative risk assessment approach can help understand relative risk contributions from different components, leak sizes, consequences, and human actions, to prioritize risk reduction strategies and balance these parameters. The outcomes of this report may be useful for a variety of stakeholders working in the hydrogen, transportation, vehicle, and aviation sector, including those responsible for aspects like facility design, operations, and regulations. There is not a single value of risk that determines whether a hypothetical system is “safe” or not. The insights about risk mitigations may be leveraged, and the quantitative risk assessment approach can be applied to other case studies to understand risk priorities and contributions specific to different FCEB and hydrogen facility uses.

08 HYDROGEN↗
DOE OSTI2020

A genomic catalog of Earth’s microbiomes

Abstract The reconstruction of bacterial and archaeal genomes from shotgun metagenomes has enabled insights into the ecology and evolution of environmental and host-associated microbiomes. Here we applied this approach to >10,000 metagenomes collected from diverse habitats covering all of Earth’s continents and oceans, including metagenomes from human and animal hosts, engineered environments, and natural and agricultural soils, to capture extant microbial, metabolic and functional potential. This comprehensive catalog includes 52,515 metagenome-assembled genomes representing 12,556 novel candidate species-level operational taxonomic units spanning 135 phyla. The catalog expands the known phylogenetic diversity of bacteria and archaea by 44% and is broadly available for streamlined comparative analyses, interactive exploration, metabolic modeling and bulk download. We demonstrate the utility of this collection for understanding secondary-metabolite biosynthetic potential and for resolving thousands of new host linkages to uncultivated viruses. This resource underscores the value of genome-centric approaches for revealing genomic properties of uncultivated microorganisms that affect ecosystem processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Cy-Phy ADS: Cyber Physical Anomaly Detection Framework for EV Charging Systems

Today’s large-scale Electric Vehicle (EV) infrastructures are heavily dependent on information communication technologies to maintain their operation and to support communication within sub-system components as well as the outside world. These technologies are vulnerable to various cyber and physical threats. Timely identification and mitigation of these threats are critical for improving human safety, avoiding economic losses, and preventing catastrophic system failures. By addressing this, our work presents a ResNet Autoencoder (AE) based Cyber-Physical Anomaly Detection System (Cy-Phy ADS) for detecting anomalies in EV Controller Area Network (CAN) protocol communication. It consists of four main components: Cyber-Physical Feature Extractor, ResNet AE-based Anomaly Detection Framework, Cyber-Physical Health Metric (CPHM), and Visualization Dashboard. The presented framework was trained and tested using CAN data collected from the EV charging system testbed at the Idaho National Laboratory. The presented Cy-Phy ADS compared against six widely used unsupervised anomaly detection algorithms: One Class Support Vector Machine (OCSVM), Variational Autoencoder (VAE), LSTM Autoencoder (LSTM AE), Isolation Forest (IForest), Principle Component Analysis (PCA) and Local Outlier Factor (LOF). Here the presented approach showed the highest accuracy among the compared methods. Further, the proposed approach showed comparable performance in terms of precision, F1, and False positive rate. It also showed the lowest training and inference time compared to the neural network-based baseline algorithms compared against with. Additionally, the Cy-Phy ADS has advantages such as unsupervised training, the ability to provide a holistic metric for system health characterization, and non-linear feature extraction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Progress toward a universal biomedical data translator

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Next-Level Energy Management in Manufacturing: Facility-Level Energy Digital Twin Framework Based on Machine Learning and Automated Data Collection

This research introduces an energy prediction framework at the facility level supported by automated data collection and machine learning models. It investigates whether reducing the prediction time scale allows for applying more complex machine learning techniques and if those techniques improve the prediction accuracy. The primary advantages of this framework lie in its automation of the energy prediction process and its provision of real-time energy data suitable for use in energy dashboards or digital twins. A sitewide dataset was created by combining 15 min energy and daily production data of five shops—assembly, battery, body (electric), body (gas), and paint—from a globally recognized electric vehicle manufacturer. Various machine learning models were evaluated on daily, weekly, and monthly datasets, including, in increasingly complex order: naïve, simple linear regression, net regularized generalized linear regression, principal component regression, k-nearest neighbor, random forest, and Bayesian regularized neural network. Compared to the current state-of-the-art energy consumption prediction for the industrial facility level, this research investigates more complex models and smaller time intervals for higher accuracy. The findings revealed that the more complex monthly models require a minimum of a year and a half of data to operate, while weekly models demand a year of data to achieve improved accuracy. Daily models can operate with only six months of data but exhibit poor performance due to reduced prediction accuracy of production. Key challenges identified include access to reliable, high-quality energy and production data and the initial demand for human labor.

digital twin↗
DOE OSTI2022

Tritium Fires: Simulation and Safety Assessment

This is the Sandia report from a joint NSRD project between Sandia National Labs and Savannah River National Labs. The project involved development of simulation tools and data intended to be useful for tritium operations safety assessment. Tritium is a synthetic isotope of hydrogen that has a limited lifetime, and it is found at many tritium facilities in the form of elemental gas (T 2 ). The most serious risk of reasonable probability in an accident scenario is when the tritium is released and reacts with oxygen to form a water molecule, which is subsequently absorbed into the human body. This tritium oxide is more readily absorbed by the body and therefore represents a limiting factor for safety analysis. The abnormal condition of a fire may result in conversion of the safer T 2 inventory to the more hazardous oxidized form. It is this risk that tends to govern the safety protocols. Tritium fire datasets do not exist, so prescriptive safety guidance is largely conservative and reliant on means other than testing to formulate guidelines. This can have a consequence in terms of expensive and/or unnecessary mitigation design, handling protocols, and operational activities. This issue can be addressed through added studies on the behavior of tritium under representative conditions. Due to the hazards associated with the tests, this is being approached mainly from a modeling and simulation standpoint and surrogate testing. This study largely establishes the capability to generate simulation predictions with sufficiently credible characteristics to be accepted for safety guidelines as a surrogate for actual data through a variety of testing and modeling activities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Realization of thousand-second improved confinement plasma with Super I-mode in Tokamak EAST

Mastering nuclear fusion, which is an abundant, safe, and environmentally competitive energy, is a great challenge for humanity. Tokamak represents one of the most promising paths toward controlled fusion. Obtaining a high-performance, steady-state, and long-pulse plasma regime remains a critical issue. Recently, a big breakthrough in steady-state operation was made on the Experimental Advanced Superconducting Tokamak (EAST). A steady-state plasma with a world-record pulse length of 1056 s was obtained, where the density and the divertor peak heat flux were well controlled, with no core impurity accumulation, and a new high-confinement and self-organizing regime (Super I-mode = I-mode + e-ITB) was discovered and demonstrated. These achievements contribute to the integration of fusion plasma technology and physics, which is essential to operate next-step devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Performance Assessment for the E-Area Low Level Radioactive Waste Disposal Facility at the Savannah River Site: Chapter 1

This report documents the revised Performance Assessment (PA) analysis for the E-Area Low-Level Waste Facility (ELLWF) at the United States (U.S.) Department of Energy (DOE) Savannah River Site (SRS). A PA analysis is required for DOE-operated facilities that dispose of low-level radioactive waste. PA analyses simulate (1) the release of radionuclides from the disposal site after facility closure, (2) transport of those contaminants through the environment, and (3) exposure/impacts to potential receptors. The purpose of the PA analysis is to demonstrate that the facility is operated in a manner that ensures long-term environmental protection after facility closure, thereby providing for the protection of public health and safety in limiting doses to a hypothetical member of the public (MOP) or an inadvertent human intruder (IHI). DOE Manual (M) 435.1-1, Chg. 3, Radioactive Waste Management (U.S. DOE, 2021b) establishes quantitative post-closure environmental impact limits and requires a facility-specific PA analysis to demonstrate compliance with these limits for DOE low-level waste (LLW) disposed of after September 26, 1988. These limits are defined in terms of human health (e.g., dose limits) with respect to radioactive constituents in the waste.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Effect of iodides on thermal behavior and phase partitioning in LiCl-KCl

Liquid-fueled molten salt reactors (MSRs) are designed to operate with fissile materials and, ultimately, fission products dissolved in the primary molten salt coolant. Understanding the speciation and transport of iodine—a high-yield fission product—is essential because this element’s accidental release poses significant environmental concerns due to its capacity to be readily absorbed by the human thyroid gland. Here, we report the impact of iodide species (LiI and KI) on phase transitions, phase distribution, and phase stability in LiCl–KCl-eutectic salt mixtures. The study employed a combination of computational and experimental techniques, including thermodynamic FactSage calculations, differential scanning calorimetry, and high-temperature X-ray diffraction. The results indicate that the presence of iodide (10–25 wt%) significantly alters the melting behavior of the LiCl–KCleutectic system. Adding 10 wt% LiI has a more-pronounced effect than 10 wt% KI, as LiI converts to KI, leading to formation of LiCl, thereby, altering the LiCl-KCl ratio which significantly affects the melting temperature of the mixture. Furthermore, the evolution of crystalline structure, solid-fraction composition, and the dynamics of mixed-halide solid–liquid partitioning as a function of temperature indicate the potential for selective iodide separation from chloride-salt mixtures via solid–liquid separation techniques. Overall, the presented findings provide valuable insights that are beneficial for the design and operation of MSRs, as well as for the safe handling and effective processing of used nuclear fuel using advanced pyrochemical techniques.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Assessment of Wind Energy Resource Potential for Future Human Missions to Mars

Energy sustainability and redundancy for surface habitats, life support systems and scientific instrumentation represent one of the highest-priority issues for future crewed missions to Mars. However, power sources utilized for the current class of robotic missions to Mars may be potentially dangerous near human surface habitats (for example, nuclear) or lack stability on diurnal or seasonal timescales (for example, solar) that cannot be easily compensated for by power storage. Here, we evaluate the power potential for wind turbines as an alternative energy resource on the Mars surface. Using a state-of-the-art Mars global climate model, we analyse the total planetary Martian wind potential and calculate its spatial and temporal variability. We find that wind speeds at some proposed landing sites are sufficiently fast to provide a stand-alone or complementary energy source to solar or nuclear power. While several regions show promising wind energy resource potential, other regions of scientific interest can be discarded based on the natural solar and wind energy potential alone. We demonstrate that wind energy compensates for diurnal and seasonal reductions in solar power particularly in regions of scientific merit in the midlatitudes and during regional dust storms. Critically, proposed turbines stabilize power production when combined with solar arrays, increasing the percent time that power exceeds estimated mission requirements from ~40% for solar arrays alone to greater than 60-90% across a broad fraction of the Mars surface. We encourage additional study aimed at advancing wind turbine technology to operate efficiently under Mars conditions and to extract more power from Mars winds.

atmospheric dynamics↗
DOE OSTI2021

Canary

CANARY is a wearable environmental sampling device that is small, lightweight, and low-power. CANARY integrates a novel mechanism that uses magnets rather than powered pumps to mimic human breathing. In its current form, CANARY breathes, but sensor integration can easily turn this into a standalone device for use in airborne detection of chemicals, biologicals, viruses, etc. CANARY has a small footprint and requires little power to operate enabling it to be used in various environments. Los Alamos researcher have developed a working prototype of the air intake system and are seeking licensees for the current air intake, validation outside the lab, or further development to integrate biosensors as desired.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Primer to SWIFt 2.0 Technical Information Sheets

More than 16,000 publicly owned municipal wastewater treatment plants (WWTPs) in the U.S. treat a wide range of wastewater (WW) flows ranging from <0.5 to >200 MGD. Municipal WWTPs act as the last barrier before exposure to water-borne diseases and contaminants, thus serving as a critical infrastructure protecting human health and the environment. A recent survey by the American Society of Civil Engineers assigned the U.S.’s WW infrastructure a grade of D+ because of the number of improvements needed for end-of-life infrastructure with operating conditions being over or under design capacities. With increasing population growth and urbanization, the municipal WW sector needs to grow resiliently and sustainably to accommodate increasing sewer flows in the wake of new infrastructure needs and global priorities. In addition, this growth will be affected by rising concerns over constrained resources (e.g., electricity, energy, and water) and regulations. This necessitates molding the WW sector toward sustainability- and resiliency-related planning and innovations to address current and future needs while simultaneously producing profitable by-products.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Quantum dot lightning detection and warning system and method

A quantum dot (QD) lightning detection and warning (LDW) system and method. This LDW system and method find broader applicability to spark and other transient optical event detection as well. The QDs are operable for receiving ultraviolet (UV), infrared (IR), visible, x-ray, and/or gamma ray radiation emanating from lightning or the like and generating visible radiation that may be detected and utilized to generate topological event information, such that property, human life, and the like may be safeguarded.

Bahritdinov, Farhod↗
DOE OSTI2025

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗