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This poster provides an update on enhancements to the Future Automotive System Technology Simulator (FASTSim) and data updates to the Transportation Secure Data Center (TSDC).
The Transportation Secure Data Center (TSDC) provides free, web-based access to detailed transportation data from a variety of travel surveys conducted across the nation. While preserving the privacy of survey participants, this online repository makes vital transportation data broadly available to users from the comfort of their own desks via a secure online connection. Maintained by the U.S. Department of Energy's National Renewable Energy Laboratory (NREL) in partnership with the U.S. Department of Transportation (DOT), the TSDC houses data from travel surveys and studies conducted using global positioning system (GPS) devices. It features millions of data points - second-by-second GPS readings, vehicle characteristics (if applicable), and demographics - for all modes of travel. NREL screens the initial data for quality control, translates each data set into a consistent format, and interprets the data for spatial analysis. NREL's processing routines add information on vehicle fuel economy and road grades and join data points to the road network.
The Transportation Secure Data Center is a centralized repository for high-resolution transportation data from hundreds of travel and transit surveys and studies. It makes vital transportation data broadly available to users while preserving the privacy of survey participants. It houses surveys and studies conducted by state departments of transportation, metropolitan planning organizations, transit agencies, cities, and other public agencies. Meanwhile, the Livewire Data Platform empowers research, industry, and academic partners to easily and securely preserve, maintain, share, discover, and gain access to transportation and mobility data. Livewire accommodates a range of datasets, including behavioral, experimental, model, analytical, and raw data at the vehicle, traveler, and system levels. Datasets support mobility research and planning spanning urban science, connected and automated vehicles, fueling and charging infrastructure, mobility decision science, multimodal transportation, vehicle efficiency, and more.
Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time. Our models decompose anomaly scores into the contributions of individual user behavior features for increased interpretability to aid analysts reviewing potential cases of insider threat. Using the CERT Insider Threat Dataset v6.2 and threat detection recall as our performance metric, our novel deep and recurrent neural network models outperform Principal Component Analysis, Support Vector Machine and Isolation Forest based anomaly detection baselines. For our best model, the events labeled as insider threat activity in our dataset had an average anomaly score in the 95.53 percentile, demonstrating our approach's potential to greatly reduce analyst workloads.
The requirements for U.S. nuclear power plants to maintain a large on-site physical security force contribute to their high operational costs. The cost of maintaining the current physical security posture is approximately 10% of the overall operation and maintenance budget for commercial nuclear power plants. The goal of the Light Water Reactor Sustainability (LWRS) program’s physical security pathway is to develop tools, methods, and technologies and provide the technical basis for an optimized physical security posture. The conservatisms built into current security postures may be analyzed and minimized in order to reduce security costs while still ensuring adequate security and operational safety. The research performed at Idaho National Laboratory within LWRS program’s physical security pathway has successfully developed a dynamic force-on-force modeling framework using various computer simulation tools and integrating them with the dynamic assessment Event Modeling Risk Assessment using Linked Diagrams (EMRALD) tool. This document provides an update on the progress in applying a dynamic computational framework that links results from a commercially available force-on-force simulation tool, a commercially available thermal-hydraulic tool, and EMRALD to an operating commercial nuclear power plant. This report is only a summary of the progress and does not contain specific modeling results as those contain sensitive security information. This process of including plant procedures and multiple analysis results is being called Modeling and Analysis for Safety Security using Dynamic EMRALD Framework or MASS-DEF. Previous reports described how a user could integrate their plant-specific force-on-force models with the dynamic simulation tool EMRALD, model operator actions, integrate with probabilistic risk assessment tools, such as CAFTA (Computer Aided Fault Tree Analysis System) or SAPHIRE (Systems Analysis Programs for Hands-on Integrated Reliability Evaluations), and with thermal-hydraulic tools, such as RELAP-5. Previous reports applied various combinations of available simulations codes with EMRALD using generic plant models to demonstrate how to perform the analysis. This report documents the results of applying the dynamic computational framework to an actual nuclear facility using their security scenarios and timelines. This report does not contain any plant's sensitive information and/or Safeguards Information. The purpose of this study was to verify that results achieved using generic models are similar to actual plant results and to refine our guidance on the use of the framework. This assessment enables further analysis, such as what-if scenarios and staff-reduction evaluation, thereby optimizing physical security at plants.
The Nuclear Security Enterprise, managed by the National Nuclear Security Administration - a semiautonomous agency within the Department of Energy - has been associated with numerous assessments with respect to the estimating, management capabilities, and practices pertaining to nuclear weapon modernization efforts. This report identifies challenges in estimating and analyzing the Nuclear Security Enterprise through an analysis of analogous timeframe conditions utilizing two types of nuclear weapon data - (1) a measure of effort and (2) a function of time. The analysis of analogous timeframe conditions that utilizes only two types of nuclear weapon data yields four summary observations that estimators and analysts of the Nuclear Security Enterprise will find useful.
This study explores the intersection of digitalization and privacy within the energy sector, focusing on the emerging challenges and opportunities presented by integrating Distributed Energy Resources (DERs) and advanced metering infrastructure. The need for robust digital privacy measures has become crucial as the energy industry evolves towards a more decentralized, digitalized, and decarbonized future. This study delves into four cutting-edge privacy-preserving technologies—Homomorphic Encryption (HE), Secure Multiparty Computation (SMPC), Differential Privacy (DP), and Federated Learning (FL)—each offering unique solutions to safeguard consumer data by increasing digital connectivity and data exchange. Through a detailed examination of these methods, the study explains how each technology operates, its applications within the energy sector, and the specific privacy challenges it addresses. Homomorphic Encryption allows for secure computations on encrypted data, enabling data analysis without compromising privacy. Secure Multiparty Computation enables collaborative data analysis across different entities while protecting the confidentiality of the inputs. Differential Privacy introduces randomness into the assembled data set, preventing the identification of individual records in statistical databases. Lastly, Federated Learning offers a paradigm shift in data analysis, where machine learning models are trained at the edge, minimizing the centralization of sensitive data. The research underscores the significance of implementing these privacy-enhancing technologies to comply with strict data protection regulations, foster consumer trust, and enhance the security of the energy infrastructure. By providing a comprehensive overview of these methodologies and their practical implications for the energy sector, this study aims to contribute to the ongoing discourse on digital privacy, offering insights into how the energy industry can navigate the complexities of data privacy in the digital age.
State Energy Offices and Public Utility Commissions rely on timely, accurate, and actionable information to perform their energy emergency response duties and execute their roles as state energy security planners. In support of this need, the National Association of State Energy Officials (NASEO) and the National Association of Regulatory Utility Commissions (NARUC) hosted an Energy Security and Data Analysis Workshop in Washington, DC to identify energy security response and planning data sources; and to share successful methods of data use and integration in state, federal, and private sector tools. Following the workshop, NASEO and NARUC hosted two topical data-centric webinars based on state priorities to identify best practices in Geographic Information Systems (GIS) and Crisis Communications programs leveraged in energy assurance planning and response. The Crisis Communications webinar covered best practice tactics for how states can respond during energy emergencies and other crises. Based on the workshop and webinars, this document summarizes commonly used data sources and includes frequently asked questions which may be used by state energy officials (i.e., consisting of staff from Public Utility Commissions and Governor-designated State Energy Offices) to help guide them in developing or improving their Crisis Communications capabilities. A strong public information program is a key crisis management tool. Timely and accurate information helps prevent confusion and uncertainty and encourages public support and cooperation. As energy subject matter experts, state energy officials are vital in the distilling, clarifying, and conveying energy sector information and implications to decision-makers and the public. Other participants in an effective public information program include the Governor’s Office, other state agencies, local governments, energy providers, local businesses, state legislature, and the federal government. It is essential to provide stakeholders and the public with information about the nature, severity, and duration of an emergency because inadequate understanding and awareness can lead to undesirable actions that could further exacerbate the situation. Before a state government can provide information to the public, it must gather information, describe the emergency accurately, and develop recommendations to manage the situation.
Nuclear security modeling applications can often depend on very different types of lower fidelity data with significant gaps and inconsistencies. Analyzing the impact of these uncertainties on specific nuclear materials, configurations, and detection technologies provides valuable insight when determining where data improvements are needed the most. This paper describes the development and application of emerging uncertainty analysis tools that can be used to perform nuclear data uncertainty analysis and to identify and rank the impacts of individual nuclear data on national security applications. These tools constitute the framework of a comprehensive data uncertainty analysis capability that can provide improved quantitative and objective information on nuclear data needs. This information can be used to augment subject matter expert judgement. This cross-cutting research and development (R&D) capability can be applied to identify gaps and weaknesses in nuclear data to help prioritize future data investments. This in turn can help maximize benefits for specific agency missions. Lessons learned from the analysis of an illustrative example problem involving the measurement of plutonium in a neutron multiplicity counter are described herein, and areas for further methods and nuclear data uncertainty data development are provided.
This fact sheet describes the Transportation Secure Data Center (TSDC) - an NREL-operated resource that provides secure access to detailed GPS travel data for valuable research purposes in a way that protects original participant privacy.
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This paper explores a real security vulnerability and patch management dataset from an electric utility in order to shed light on characteristics of the vulnerabilities that electric utility assets have and how they are remediated in practice. Specifically, it first analyzes the distribution of vulnerabilities over software, assets, and other metric. Then it analyzes how vulnerability features affect remediate actions.
Modern cyber security operations collect an enormous amount of logging and alerting data. While analysts have the ability to query and compute simple statistics and plots from their data, current analytical tools are too simple to admit deep understanding. To detect advanced and novel attacks, analysts turn to manual investigations. While commonplace, current investigations are time-consuming, intuition-based, and proving insufficient. Our hypothesis is that arming the analyst with easy-to-use data science tools will increase their work efficiency, provide them with the ability to resolve hypotheses with scientific inquiry of their data, and support their decisions with evidence over intuition. To this end, we present our work to build IDEAS (Interactive Data Exploration and Analysis System). We present three real-world use-cases that drive the system design from the algorithmic capabilities to the user interface. Finally, a modular and scalable software architecture is discussed along with plans for our pilot deployment with a security operation command.
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This report details seismic ambient noise analysis to improve seismic velocity estimates of the southeastern Nevada National Security Site (NNSS). We compare two different methods for estimating surface wave dispersion curves from ambient noise cross-correlations: frequency time analysis (FTAN) and Aki’s cross-spectral method (XSpec). We find that XSpec performs better for our local-regional dataset and frequency content. Using phase velocity estimates from XSpec, we build a preliminary phase velocity dispersion dataset, which contains data for 1,054 station pairs and 8,905 discrete phase velocity measurements for periods between 0.5 and 13 s. This phase velocity dataset will be incorporated into an updated local-regional P and S wave velocity model of the southeastern NNSS in the future and is expected to improve upon shallow velocity estimates.
The NSLS-II network and computing infrastructure has been significantly updated recently. The re-IP process in 2020-2021 enabled the NSLS-II network to be routable to the rest of the BNL campus. Then, standardization of the operating systems and deployment procedures helped to deliver a consistent environment to workstations and servers used by all NSLS-II beamlines. In particular, the RedHat Enterprise Linux 8 was deployed to 700+ machines using the RedHat Satellite infrastructure management product, and all critical services (IOCs, databases, etc.) were migrated to the new OS. NFS users’ home directories are consistent across all of the machines, which eliminates the need for the individual configuration of the user environment on each host. The standard suite of software packages is available to the beamline staff and users, which includes the system packages (deployed via RPM) as well as the conda environments for data acquisition and analysis. Security measures were implemented to comply with the industry standards, which include multi-factor authentication (using Duo), secure screen lock for the beamline machines, and advanced access control to the experimental data that is stored in shared central storage available on all hosts. These major enhancements facilitated sharing the experimental data (currently for a number of selected beamlines, with a plan to extend it to the whole facility in the nearest future) with the users via an externally facing JupyterHub instance. The beamlines keep using the Bluesky data acquisition framework to orchestrate their experiments, and the new infrastructure enabled them to use a next-generation data access library called tiled.
The National Nuclear Security Administration’s Office of Nuclear Smuggling Detection and Deterrence (NSDD) has facilitated the installation of more than 3,728 radiation portal monitors (RPMs) at 660 sites in 63 countries. This collection of RPMs represents the world’s largest network of radiation detectors and provides one element of the defense-in-depth approach that supports the Global Nuclear Detection Architecture. These systems support NSDD’s mission to build partner country capability to deter, detect, and interdict the illicit transport of radiological and fissile material through strategic points of entry and exit at seaports, airports, and border crossings.NSDD works collaboratively with partner countries and international organizations to optimize the operation of these RPMs. The large amount of data provided by NSDD partner countries (summarized in the banner to the right) highlights the close cooperation and partnerships NSDD has built with 63 countries around the world. Thirty-two of these countries shared at least some RPM-related data with NSDD in fiscal year (FY) 2018. This significant level of data sharing is a key element that distinguishes the NSDD office as unique among nuclear nonproliferation programs and initiatives: NSDD can provide specific, objective, data-driven decisions and support for sustaining the radiation detection systems it helped deploy. This data analysis report summarizes and aggregates the RPM data provided to the NSDD office for analysis and review in FY 2018.The data can be used to describe RPM performance and characterize the wide diversity of NSDD deployment sites. For example, NSDD deploys detector systems across sites with natural background radiation levels that can vary by a factor of up to nine from site to site. Some RPM lanes have few occupancies, whereas others have up to approximately 8,000 occupancies per day, and the different types of cargo traveling through a site can result in site-wide alarm rates that range from near 0% at some sites to above 3% at others. Based on the data received, the global average uptime for NSDD RPMs was 97% for FY 2018. NSDD takes all these factors into account in making recommendations to partner countries on how to most effectively manage and maintain site operations. NSDD utilizes reports and other information products created by ORNL Radiation Inspection Systems Team (RIST) data analysts to efficiently allocate the resources needed to detect and ultimately interdict illicit nuclear and radiological material.