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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Facility Cybersecurity Framework Best Practices Version 2.0

Federal facilities are increasingly adopting automation and connecting to the Internet creating an energy-internet-of-things environment that converges operational technology (OT) and information technology (IT). Today's buildings increasingly weave together networked sensors and cyber and physical systems that enable data to be collected, aggregated, exchanged, stored and monetized in new ways. Building technological advances have created new energy technology, services, markets and value creation opportunities (e.g. transactive energy, two-way grid communications, machine learning, and increased use of renewable and distributed energy resources). But as larger data sets are being exchanged at faster speeds between an increasing number of OT systems, it becomes more difficult to protect the security of the data lifecycle and the physical equipment it interacts with. These challenges are especially difficult to overcome because the economic and environmental gain (interoperability, big data, social networks and ubiquitous information sharing) are driving these prominent trends in the digital age. Often cybersecurity is an afterthought.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2017

User Needs and Advances in Space Wireless Sensing and Communications

Decades of space exploration and technology trends for future missions show the need for new approaches in space/planetary sensor networks, observatories, internetworking, and communications/data delivery to Earth. The User Needs to be discussed in this talk includes interviews with several scientists and reviews of mission concepts for the next generation of sensors, observatories, and planetary surface missions. These observatories, sensors are envisioned to operate in extreme environments, with advanced autonomy, whereby sometimes communication to Earth is intermittent and delayed. These sensor nodes require software defined networking capabilities in order to learn and adapt to the environment, collect science data, internetwork, and communicate. Also, some user cases require the level of intelligence to manage network functions (either as a host), mobility, security, and interface data to the physical radio/optical layer. For instance, on a planetary surface, autonomous sensor nodes would create their own ad-hoc network, with some nodes handling communication capabilities between the wireless sensor networks and orbiting relay satellites. A section of this talk will cover the advances in space communication and internetworking to support future space missions. NASA's Space Communications and Navigation (SCaN) program continues to evolve with the development of optical communication, a new vision of the integrated network architecture with more capabilities, and the adoption of CCSDS space internetworking protocols. Advances in wireless communications hardware and electronics have enabled software defined networking (DVB-S2, VCM, ACM, DTN, Ad hoc, etc.) protocols for improved wireless communication and network management. Developing technologies to fulfil these user needs for wireless communications and adoption of standardized communication/internetworking protocols will be a huge benefit to future planetary missions, space observatories, and manned missions to other planets.

Kegege, Obadiah↗
DOE OSTI2021

A Taxonomic Classification Approach for Global Spatio-temporal Data

The World Bank, World Health Organization, and other major vendors collectively provide thousands of global time series datasets that focus on issues of the environment, public health, economics, violence, education, and national security. Sorting these data into meaningful information requires the use of data mining techniques to cluster trends into an orderly and manageable number of cases. The World SpatioTemporal Analytics and Mapping (WSTAMP) project database (wstamp.ornl.gov) was developed to spatiotemporally harmonize global vendor data (23,300+ attributes, 200+ locations, 50+ years). Within the WSTAMP analytical environment, Dynamic Time Warping (DTW) has been a highly effective data-driven approach for clustering and mapping these time series into national spatiotemporal behavior maps. Two significant properties have surfaced from this work. First, several recognizable cluster patterns have emerged and persist across a range of locations, attributes, and time frames (e.g., increasing, decreasing, rebounding, peak, oscillating). Secondly, practitioners engaging WSTAMP have noted the explanatory and anticipatory value of these patterns and articulated particular interest in detecting them within the spatiotemporal cube. This need was addressed by shifting DTW-based clustering from an open ended, data-driven implementation to a taxonomic pattern matching approach. This paper presents the method including implementation strategies for visualization and human computer interaction and applies the approach to a sample data set and concludes with next steps.

Stewart, Robert↗
NASA NTRS2020

CyberGAN: Generating High-fidelity Cybersecurity Data With Generative Adversarial Networks

Machine learning for cyber defense offers the promise of detecting adversarial activity against the ground data systems managing critical space assets. A fundamental challenge facing machine learning research in cybersecurity is the lack of high-fidelity, shareable datasets for robust evaluation and testing of machine learning-based solutions. High-fidelity, real-world datasets are necessary for reliable benchmarking of nominal system behavior and malicious activity. Unfortunately, such realistic datasets of both nominal and adversarial activity are rarely shared publicly by data owners due to security and privacy concerns. Besides, the available adversarial data is sparse, which makes training models on malicious activity much harder. This situation has impeded and continues to impede the research and successful adoption of machine learning methods for cyber defense. Researchers have dealt with this problem by generating data within a low-fidelity lab environment, using classified and thus unshareable datasets, or downloading low-fidelity public datasets made available by others. We propose an innovative solution to the problem by employing machine learning methods to generate high-fidelity data. Specifically, we propose the use of Generative Adversarial Networks (GANs) to generate high-fidelity data for cybersecurity purposes. GANs have found successful image processing and natural language applications, but have not yet been investigated for cyber data generation. Our proposed approach first involves training the `discriminator' network of the GAN with a sample of real-world data consisting of malicious and nominal samples. We then use the `generator' network to generate new high-fidelity data samples consisting of an appropriate mix of malicious and nominal activity. We demonstrate applications of our architecture by generating high-fidelity cybersecurity data containing both malicious and nominal samples. We thoroughly evaluate the fidelity of our generated data using heuristics and evaluate its usefulness for machine learning applications using three different datasets. Overall, our approach results in high-fidelity, shareable datasets.

Zhang, Yuening↗
DOE OSTI2020

Workshop Report for Next-Gen AI for Proliferation Detection: Accelerating the Development and Use of Explainability Methods to Design AI Systems Suitable for Nonproliferation Mission Applications

Artificial intelligence (AI) promises powerful new capabilities in an expansive array of applications. One area is proliferation detection, where AI can provide transformative tools to achieve objectives currently inaccessible using conventional methods. In particular, AI affords the opportunity to use new indicators and process massive amounts of heterogeneous data that can increase sensitivity to proliferant activities and push proliferation detection to the earliest possible stages. However, existing AI tools are not ready to achieve such potential, falling short in regard to some important considerations involved in proliferation detection. One crucial aspect that requires improvement is the explainability of the most powerful AI algorithms (i.e., understanding how the algorithms actually arrive at their conclusions)-the lack of which prevents widespread adoption in national security missions. The Data Science and AI portfolio within the National Nuclear Security Administration's Office of Defense Nuclear Nonproliferation Research and Development is driving the development of next-generation AI for proliferation detection through the expertise and work of the national laboratories and partners in academia. The "Next-Gen AI for Proliferation Detection: Accelerating the Development and Use of Explainability Methods to Design AI Systems Suitable for Nonproliferation Mission Applications" workshop aimed to further this objective. The meeting was held virtually on Sept. 15-16, 2020 and included more than 170 participants, primarily from the national laboratories with additional contributions from university researchers and mission partners. Each day included a keynote presentation, four technical presentations about cutting-edge research in explainability, and a panel to explore considerations in applying explainability and developing AI systems that can accelerate the transition of emerging AI technologies among partners and end users to solve critical mission questions. This report summarizes the Next-Gen AI for Proliferation Detection workshop's content and findings.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2014

Re-designing the PhEDEx Security Model

PhEDEx, the data-placement tool used by the CMS experiment at the LHC, was conceived in a more trusting time. The security model provided a safe environment for site agents and operators, but offerred little more protection than that. Data was not sufficiently protected against loss caused by operator error or software bugs or by deliberate manipulation of the database. Operators were given high levels of access to the database, beyond what was actually needed to accomplish their tasks. This exposed them to the risk of suspicion should an incident occur. Multiple implementations of the security model led to difficulties maintaining code, which can lead to degredation of security over time. In order to meet the simultaneous goals of protecting CMS data, protecting the operators from undue exposure to risk, increasing monitoring capabilities and improving maintainability of the security model, the PhEDEx security model was redesigned and re-implemented. Security was moved from the application layer into the database itself, fine-grained access roles were established, and tools and procedures created to control the evolution of the security model over time. In this paper we describe this work, we describe the deployment of the new security model, and we show how these enhancements improve security on several fronts simultaneously.

Huang, C.-H.↗
DOE OSTI2023

Privacy-preserving federated learning: Application to behind-the-meter solar photovoltaic generation forecasting

Here, the growing usage of decentralized renewable energy sources has made accurate estimation of their aggregated generation crucial for maintaining grid flexibility and reliability. However, the majority of distributed photovoltaic (PV) systems are behind-the-meter (BTM) and invisible to utilities, leading to three challenges in obtaining an accurate forecast of their aggregated output. Firstly, traditional centralized prediction algorithms used in previous studies may not be appropriate due to privacy concerns. There is therefore a need for decentralized forecasting methods, such as federated learning (FL), to protect privacy. Secondly, there has been no comparison between localized, centralized, and decentralized forecasting methods for BTM PV production, and the trade-off between prediction accuracy and privacy has not been explored. Lastly, the computational time of data-driven prediction algorithms has not been examined. This article presents a FL power forecasting method for PVs, which uses federated learning as a decentralized collaborative modeling approach to train a single model on data from multiple BTM sites. The machine learning network used to design this FL-based BTM PV forecasting model is a multi-layered perceptron, which ensures privacy and security of the data. Comparing the suggested FL forecasting model to non-private centralized and entirely private localized models revealed that it has a high level of accuracy, with an RMSE that is 18.17% lower than localized models and 9.9% higher than centralized models.

14 SOLAR ENERGY↗
DOE OSTI2011

Communication Pathways in the Light Water Reactor Sustainability Online Monitoring Project

Implementation of online monitoring and prognostics in existing U.S. nuclear power plants will involve coordinating the efforts of national laboratories, utilities, universities, and private companies. Large amounts of operational data, including failure data, are necessary for the development and calibration of diagnostic and prognostic algorithms. The ability to use data from all available resources will provide the most expeditious avenue to implementation of online monitoring in existing NPPs; however, operational plant data are often considered proprietary. Secure methods for transferring and storing data are discussed, along with a potential technology for implementation of online monitoring.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2014

NASA Unmanned Aircraft (UA) Control and Non-Payload Communication (CNPC) System Waveform Trade Studies

Unmanned Aircraft Systems (UAS) 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 due to the lack of a common understanding of what is required to safely operate UAS in the National Airspace System (NAS). To address this deficiency, NASA has established a project called UAS Integration in the NAS (UAS in the NAS), under the Integrated Systems Research Program (ISRP) of the Aeronautics Research Mission Directorate (ARMD). This project provides an opportunity to transition concepts, technology, algorithms, and knowledge to the Federal Aviation Administration (FAA) and other stakeholders to help them define the requirements, regulations, and issues for routine UAS access to the NAS. The safe, routine, and efficient integration of UAS into the NAS requires new radio frequency (RF) spectrum allocations and a new data communications system which is both secure and scalable with increasing UAS traffic without adversely impacting the Air Traffic Control (ATC) communication system. These data communications, referred to as Control and Non-Payload Communications (CNPC), whose purpose is to exchange information between the unmanned aircraft and the ground control station to ensure safe, reliable, and effective unmanned aircraft flight operation. A Communications Subproject within the UAS in the NAS Project has been established to address issues related to CNPC development, certification and fielding. The focus of the Communications Subproject is on validating and allocating new RF spectrum and data link communications to enable civil UAS integration into the NAS. The goal is to validate secure, robust data links within the allocated frequency spectrum for UAS. A vision, architectural concepts, and seed requirements for the future commercial UAS CNPC system have been developed by RTCA Special Committee 203 (SC-203) in the process of determining formal recommendations to the FAA in its role provided for under the Federal Advisory Committee Act. NASA intends to conduct its research and development in keeping with this vision and associated architectural concepts. The prototype communication systems developed and tested by NASA will be used to validate and update the initial SC-203 requirements in order to provide a foundation for SC-203's Minimum Aviation System Performance Standards (MASPS).

Aircraft Communications↗
NASA NTRS2021

Research Report: Building a File Observatory for Secure Parser Development

"Parsing untrusted data is notoriously challenging.Failure to handle maliciously crafted data correctly can (anddoes) lead to a wide range of vulnerabilities. The Languagetheoretic security (LangSec) philosophy seeks to obviate the needfor developers to apply ad hoc solutions by, instead, offeringformally correct and verifiable input handling throughout thesoftware development lifecycle. One of the key components indeveloping secure parsers is a broad coverage corpus that enablesdevelopers to understand the problem space for a given formatand to use, potentially, as seeds for fuzzing and other automatedtesting. In this paper, we offer an update on the developmentof a file observatory to gather and enable analysis on a diversecollection of files at scale. Specifically, we report on the additionof a bug tracker corpus and new analytic methods on our existingcorpus."

Stonebraker, Ryan↗
NASA NTRS2022

Research Report: Progress on Building a File Observatory for Secure Parser Development

Parsing untrusted data is notoriously challenging.Failure to handle maliciously crafted data correctly can (anddoes) lead to a wide range of vulnerabilities. The Languagetheoretic security (LangSec) philosophy seeks to obviate the needfor developers to apply ad hoc solutions by, instead, offeringformally correct and verifiable input handling throughout thesoftware development lifecycle. One of the key components indeveloping secure parsers is a broad coverage corpus that enablesdevelopers to understand the problem space for a given formatand to use, potentially, as seeds for fuzzing and other automatedtesting. In this paper, we offer an update on work reportedat the LangSec 2021 conference on the development of a fileobservatory to gather and enable analysis on a diverse collectionof files at scale. The initial focus of the observatory is on PortableDocument Format (PDF) files and file formats typically embeddedin PDFs. In this paper, we report on refactoring the ingestprocess, applying new analytic methods, and improving the User Interface.

Stonebraker, Ryan↗
DOE OSTI2019

An Overview of Technologies for Individual Trip History Collection: Mobility Decision Science Pillar SMART Mobility Consortium

WholeTraveler is a project of the Department of Energy's Smart Mobility consortium that explores how an individual's attitudes and lifestyle characteristics are embodied in transportation behaviors throughout one's life trajectory. As a study at the nexus of decision science and urban systems analysis, fine-grained location data (FGLD), or records of an individual's location collected at a resolution sufficient to determine both the unique destinations a traveler visits each day and the modes used to travel between them, underlie high quality results. Moreover, the methods for collecting FGLD and the means through which they are associated with a traveler's personal attributes have significant implications for the burden on research participants, the cost and complexity of the study, as well as the confidence with which hypotheses can be validated. This paper provides a broad overview of existing technologies for FGLD collection and is intended to provide a literature review for researcher exploring similar domain areas. Technologies are categorized as either network or onboard approaches. An onboard system, such as smartphone or GPS device, sends signals to external networks to capture its own location. Conversely, network systems, such as cell towers, are comprised of sensing nodes with known locations that record when individually identifiable assets come within their range of detection. Our analysis indicates that the most sensible approach to FGLD collection, for the purposes of WholeTraveler, is to leverage onboard devices, and in particular the existing Moves or Google Maps applications for Android and iOS smartphones. Dedicated GPS devices have been widely implemented by the transportation research community and pose an alternatively acceptable form of FGLD collection. Independently developing a custom smartphone application is not recommended because of the notable challenges in establishing minimal viable functionality, as well as in ensuring compatibility across an ever-growing combination of hardware devices and software versions. No matter the technical means of data collection, the secure handling of FGLD is of critical importance.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Measuring Human Performance within Computer Security Incident Response Teams

Human performance has become a pertinent issue within cyber security. However, this research has been stymied by the limited availability of expert cyber security professionals. This is partly attributable to the ongoing workload faced by cyber security professionals, which is compound ed by the limited number of qualified personnel and turnover of personnel across organizations. Additionally, it is difficult to conduct research, and particularly, openly published research, due to the sensitivity inherent to cyber ope rations at most organizations. As an alternative, the current research has focused on data collection during cyber security training exercises. These events draw individuals with a range of knowledge and experience extending from seasoned professionals to recent college graduates to college students. The current paper describes research involving data collection at two separate cyber security exercises. This data collection involved multiple measures which included behavioral performance based on human - machine transactions and questionnaire - based assessments of cyber security experience.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

From Count Rates to Quantifying Isotopic Activities – Field Analysis of Radiation Monitoring Data

The Nevada National Security Site (NNSS) provides a comprehensive bicoastal radiological and nuclear emergency response to United States Department of Energy/National Nuclear Security Administration. A major part of the support is to provide systematic radiological search for lost or stolen sources, Radiological search is a core competency of the NNSS with its origin dating back to nuclear weapons test era. Search operations from multiple platforms is the common thread among the various NNSS assets, which include Aerial Measuring System (AMS), Maritime Support Team (MST), National Capitol Response (NCR), National Search Team (NST) and Radiological Assistance Program (RAP). Information collected and analyzed during search operations add to the actionable intelligence for the law enforcement agencies and provide valuable guidance for the tactical resolution of a nuclear or radiological crisis. Search is an intelligence and situational awareness driven operation and most often called upon during a radiological emergency, however it can be brought into play to thwart a potential threat by providing monitoring and surveillance support. The Office of Nuclear Incident Response (NA-84) serves as the technical leader in responding to and resolving nuclear and radiological threats worldwide and integrates its efforts with other NNSA stakeholders (e.g., NNSA office of Defense Nuclear Non-proliferation NA-22). The response includes expertise in the areas of radiological search, render safe, and consequence management. This article will discuss the methodologies, tools, procedures, and techniques to extract maximum radiological characterization information (isotopic composition, activities for individual isotopes, threat assessment etc.) from field monitoring or Search operation data.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

The Modern Research Data Portal: a design pattern for networked, data-intensive science

We describe best practices for providing convenient, high-speed, secure access to large data via research data portals. Here, we capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based data portal to achieve orders-of-magnitude increases in data transfer performance, support new deployment architectures that decouple control logic from data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance data enclaves and cloud-based data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, authorization, data transfer, and data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp , provides application skeletons that readers can adapt to realize their own research data portals.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

Dual-Doppler Feasibility Study

When two or more Doppler weather radar systems are monitoring the same region, the Doppler velocities can be combined to form a three-dimensional (3-D) wind vector field thus providing for a more intuitive analysis of the wind field. A real-time display of the 3-D winds can assist forecasters in predicting the onset of convection and severe weather. The data can also be used to initialize local numerical weather prediction models. Two operational Doppler Radar systems are in the vicinity of Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS); these systems are operated by the 45th Space Wing (45 SW) and the National Weather Service Melbourne, Fla. (NWS MLB). Dual-Doppler applications were considered by the 45 SW in choosing the site for the new radar. Accordingly, the 45th Weather Squadron (45 WS), NWS MLB and the National Aeronautics and Space Administration tasked the Applied Meteorology Unit (AMU) to investigate the feasibility of establishing dual-Doppler capability using the two existing systems. This study investigated technical, hardware, and software requirements necessary to enable the establishment of a dual-Doppler capability. Review of the available literature pertaining to the dual-Doppler technique and consultation with experts revealed that the physical locations and resulting beam crossing angles of the 45 SW and NWS MLB radars make them ideally suited for a dual-Doppler capability. The dual-Doppler equations were derived to facilitate complete understanding of dual-Doppler synthesis; to determine the technical information requirements; and to determine the components of wind velocity from the equation of continuity and radial velocity data collected by the two Doppler radars. Analysis confirmed the suitability of the existing systems to provide the desired capability. In addition, it is possible that both 45 SW radar data and Terminal Doppler Weather Radar data from Orlando International Airport could be used to alleviate any radar geometry issues at the NWS MLB radar, such as the "cone of silence" or beam blockage. In the event of a radar outage at one of the sites, the multi-radar algorithms would provide continuing coverage of the area through use of the data from the remaining operational radar sites. There are several options to collect, edit, synthesize and display dual-Doppler data sets. These options include commercial packages available for purchase and a variety of freeware packages available from the National Center for Atmospheric Research (NCAR) for processing raw radar data. However, evaluation of the freeware packages revealed that they do not have sufficient documentation and configuration control to be certified for 45 SW use. Additionally, a TI data line must be installed/leased from the NWS MLB office and CCAFS to enable the receipt of NWS MLB raw radar data to use in the dual-Doppler synthesis. Integration of the TI data line into the Eastern Range infrastructure that will meet the security requirements necessary for 45 SW use is time-consuming and costly. Overall evaluation indicates that establishment of the dual-Doppler capability using the existing operational radar systems is desirable and feasible with no technical concerns. Installation of such a system represents a significant enhancement to forecasting capabilities at the 45 WS and at NWS MLB. However, data security and cost considerations must be evaluated in light of current budgetary constraints. In any case, gaining the dual-Doppler capability will provide opportunities for better visualization of the wind field and better forecasting of the onset of convection and severe weather events to support space launch operations at KSC and CCAFS.

Huddleston, Lisa L.↗
DOE OSTI2025

Enabling Seamless Transitions from Experimental to Production HPC for Interactive Workflows

The evolving landscape of scientific computing requires seamless transitions from experimental to production HPC environments for interactive workflows. This paper presents a structured transition pathway developed at OLCF that bridges the gap between development testbeds and production systems. We address both technological and policy challenges, introducing frameworks for data streaming architectures, secure service interfaces, and adaptive resource scheduling for time-sensitive workloads and improved HPC interactivity. Our approach transforms traditional batch-oriented HPC into a more dynamic ecosystem capable of supporting modern scientific workflows that require near real-time data analysis, experimental steering, and cross-facility integration.

Etz, Brian [ORNL] (ORCID:0000000208554863)↗