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National Infrastructure Simulation and Analysis Center Feedback onHomeland Security Infrastructure Protection Data.
Abstract not provided.
Data privacy and security considerations for personal assistants for learning (PAL).
Abstract not provided.
Novel data visualizations of x-ray data for aviation security applications using the OTAP platform.
Abstract not provided.
Novel Data Visualizations Of X-Ray Data For Aviation Security Applications Using The OTAP Platform.
Abstract not provided.
AUTHENTICATED SENSOR INTERFACE DEVICE FOR SECURING SENSORS AND DATA TRANSMISSION
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Nevada National Security Site 2017 Data Report: Groundwater Monitoring Program Area 5 Radioactive Waste Management Site
NNSS Area 5 RWMS Groundwater and Leachate Monitoring Report for RCRA Permit NEV HW0101
NEVADA NATIONAL SECURITY SITE 2022 DATA REPORT: GROUNDWATER MONITORING PROGRAM AREA 5 RADIOACTIVE WASTE MANAGEMENT SITE
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Towards Reducing the Data Exfiltration Surface for the Insider Threat
Unauthorized data exfiltrations from both insiders and outsiders are costly and damaging. Network communication resources can be used for transporting data illicitly out of the enterprise or cloud. Combined with built-in malware copying utilities, we define this as comprising the Data Exfiltration Surface (DXS). For securing valuable data, it is desirable to reduce the DXS and maintain controls on the egress points. Our approach is to host the data in a protected enclave that includes novel software Data Diode (SDD) installed on a secured, border gateway. The SDD allows copying data into the enclave systems but denies data from being copied out. Simultaneously, it permits remote access with remote desktop and console applications. Our tests demonstrate that we are able to effectively reduce the DXS and we are able to protect data from being exfiltrated through the use of the SDD.
Use of Data Libraries for IAEA Nuclear Security Assessment Methodologies (NUSAM) [section 5.4]
Data libraries are essential for the characterization of the facility and provide the documented input which enables the facility assessment results and subsequent conclusions. Data Libraries are historical, verifiable, quantified, and applicable collections of testing data on different types of barriers, sensors, cameras, procedures, and/or personnel. Data libraries are developed and maintained as part of any assessment program or process. Data is collected during the initial stages of facility characterization to aid in the model and/or simulation development process. Data library values may also be developed through the use of state testing centers and/or site resources by testing different types of barriers, sensors, cameras, procedures, and/or personnel. If no data exists, subject matter expert opinion and manufacturer's specifications/ testing values can be the basis for initially assigning values, but are generally less reliable and lack appropriate confidence measures. The use of existing data libraries that have been developed by a state testing organization reduces the assessment costs by establishing standard delay, detection and assessment values for use by multiple sites or facilities where common barriers and alarms systems exist.
Duality-Free Decomposition Based Data-Driven Stochastic Security-Constrained Unit Commitment
To incorporate the superiority of both stochastic and robust approaches, a data-driven stochastic optimization is employed to solve the security-constrained unit commitment model. This approach makes the most use of the historical data to generate a set of possible probability distributions for wind power outputs and then it optimizes the unit commitment under the worst-case probability distribution. However, this model suffers from huge computational burden, as a large number of scenarios are considered. To tackle this issue, a duality-free decomposition method is proposed in this paper. This approach does not require doing duality, which can save a large set of dual variables and constraints, and therefore reduces the computational burden. In addition, the inner max-min problem has a special mathematical structure, where the scenarios have the similar constraint. Thus, the max-min problem can be decomposed into independent sub-problems to be solved in parallel, which further improves the computational efficiency. A numerical study on an IEEE 118-bus system with practical data of a wind power system has demonstrated the effectiveness of the proposal.
The Feasibility of Moving PMU Data in the Future Power Grid
The power grid is a complex network connecting electricity providers with their consumers. With an increasing consumer base requiring more resources and the requirement to integrate significant renewable sources, maintaining the network requires new and innovative management solutions. To manage this complexity and provide precise, real-time views of the grid, Phasor Measurement Units (PMUs) are undergoing widespread deployment. These units provide measurements as often as 60 times per second, with an accurate time identifier attached to each reading, enabling real-time monitoring of the network. However, this new capability generates much more data than the current infrastructure is designed to handle. With the expectation that there will eventually be tens of thousands of PMUs monitoring the transmission lines, the power community is looking towards accumulating multiple terabytes of data per day - several orders of magnitude beyond current data acquisition rates. This has led to questions being raised in the power community about whether or not a significant research effort is required to effectively transfer the volume of information generated by these new data streams. This paper answers that question by comparing a worst-case data generation scenario with several alternative networking protocols and historical trends in protocol advancement. Based on this analysis we are able to conclude that transferring the information between the PMUs and the resulting data repositories is feasible. We recognize that there are issues beyond transferring the data that need to be addressed such as effective access to historical data, data transfer latency, cyber security, and data analysis. There is also an extensive engineering trade-off that the power companies will need to make to decide the best mix of networking protocols for their particular PMU deployments, since that requires significant assumptions about proprietary information including deployment cost, deployment schedules, PMU locations, and available resources.
Study of Applications of Radiological Data Fusion for Nuclear Security and Non-proliferation Applications
This report describes the radiological Scene Data Fusion (SDF) technology and the potential to apply the SDF technology to a variety of problems related to nuclear non-proliferation, radiological security, and other missions related to the national nuclear security agency and the US government, more broadly. The application space was conceived by both the authors of this report and the many scientists and stakeholders they interviewed. In order to successfully apply the SDF technology to the various applications, further research and development is often necessary and those R&D steps are also outlined herein.
Cyber Infrastructure for the Smart Electric Grid
As electric power systems undergo a transformative upgrade with the integration of advanced technologies to enable the smarter electric grid, professionals who work in the area require a new understanding of the evolving complexity of the grid. Cyber Infrastructure for the Smart Electric Grid delivers a comprehensive overview of the fundamental principles of smart grid operation and control, smart grid technologies, including sensors, communication networks, computation, data management, and cyber security, and the interdependencies between the component technologies on which a smart grid's security depends. The book offers readers the opportunity to critically analyze the smart grid infrastructure needed to sense, communicate, compute, and control in a secure way.
Quantum Computing for Biomedical Computational and Data Sciences: A Joint DOE-NIH Roundtable
The overlap of quantum computing and biomedical research, while less explored, presents significant near-term opportunities. The Department of Energy (DOE) and the National Institutes of Health (NIH) are interested in exploiting the DOE community’s capabilities and expertise in quantum computing to potentially advance biomedical research, targeting fundamental studies of biological and molecular structures, understanding of human health as well as mental and physical disorders and diseases, and deriving insights from clinical data. NIH’s approach to quantum computing is guided by its Strategic Plan for Data Science, emphasizing the importance of findable, accessible, interoperable, and reusable (FAIR) data assets, security and privacy of data, and efficient computing and storage. DOE’s Office of Science (SC), and more specifically the Advanced Scientific Computing Research (ASCR) program, supports quantum information science (QIS) research, contributing to a unique portfolio of quantum computing and communications expertise. This roundtable was assembled to consider the opportunities and challenges in the near-, medium-, and long-term at the intersection of quantum computing, data science, and biomedical research and how these could be addressed through inter-agency collaboration and multi-disciplinary partnerships.