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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Novel Authentication Management for the Data Security of Smart Grid

Bidirectional wireless communication is employed in various smart grid components such as smart meters and control and monitoring applications where security is vital. The Trusted Third Party (TTP) and wireless connectivity between the smart meter and the third party in the key management-based encryption techniques for the smart grid are expected to be totally trustworthy and dependable. In a wired/wireless medium, however, a man-in-the-middle may seek to disrupt, monitor and manipulate the network, or simply execute a replay attack, revealing its vulnerability. Recognizing this, this study presents a novel authentication management (model) comprised of two layer security schema. The first layer implements an efficient novel encryption method for secure data exchange between meters and control center with the help of two partially trusted simple servers (constitutes the TTP). In this setting, one server handles the data encryption between the meter and control center/central database, and the other server administers the random sequence of data transmission. The second layer monitors and verifies exchanged data packets among smart meters. It detects abnormal packets from suspicious sources. To implement this node-to-node authentication, One class support vector machine algorithm is proposed which takes advantages of the location information as well as the data transmission history (node identification, packet size, and data transmission frequency). This schema secures data communication, and imposes a comprehensive privacy throughout the system without considerably extending the complexity of the conventional key management scheme.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Transportation Secure Data Center

The Transportation Secure Data Center (TSDC) at https//www.nrel.gov/tsdc provides free access to detailed transportation data from travel surveys and studies conducted across the nation. It features millions of data points including second-by-second GPS readings, vehicle characteristics, 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 processing routines add information on fuel economy and road grades and join data points to the road network. NREL maintains the TSDC in partnership with the U.S. Department of Transportation and the U.S. Department of Energy. Before viewing the data for the first time, you will be required to fill out a short registration form. If you use TSDC data in a publication, please notify NREL at tsdc@nrel.gov and include a citation consistent with the following format: "Transportation Secure Data Center." ([YEAR]). National Renewable Energy Laboratory. Accessed [DATE]: www.nrel.gov/tsdc.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Data Security Defense: Modeling and Detection of Synchrophasor Data Spoofing Attack for Grid Edge

Data security and cyberattack have become critical issues in the distributed power system where adversaries can swap the source information of sensors or even spoof and alter measurements. However, the cyber security of the power system is challenged by the unpredictability and stealth of the spoofing attacks. Here, to protect the data security at the grid edge, this paper developed a synchrophasor data spoofing attack detection framework based on the time-frequency feature extraction techniques including the short-time Fourier transform (STFT) and object detection network for real-time synchrophasor data categorization and spoofing attack localization. The proposed approach outperforms earlier work in terms of spoofing attack detection and offers a vital localization function employing distributed synchrophasor sensors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2012

Security Data Warehouse Application

The Security Data Warehouse (SDW) is used to aggregate and correlate all JSC IT security data. This includes IT asset inventory such as operating systems and patch levels, users, user logins, remote access dial-in and VPN, and vulnerability tracking and reporting. The correlation of this data allows for an integrated understanding of current security issues and systems by providing this data in a format that associates it to an individual host. The cornerstone of the SDW is its unique host-mapping algorithm that has undergone extensive field tests, and provides a high degree of accuracy. The algorithm comprises two parts. The first part employs fuzzy logic to derive a best-guess host assignment using incomplete sensor data. The second part is logic to identify and correct errors in the database, based on subsequent, more complete data. Host records are automatically split or merged, as appropriate. The process had to be refined and thoroughly tested before the SDW deployment was feasible. Complexity was increased by adding the dimension of time. The SDW correlates all data with its relationship to time. This lends support to forensic investigations, audits, and overall situational awareness. Another important feature of the SDW architecture is that all of the underlying complexities of the data model and host-mapping algorithm are encapsulated in an easy-to-use and understandable Perl language Application Programming Interface (API). This allows the SDW to be quickly augmented with additional sensors using minimal coding and testing. It also supports rapid generation of ad hoc reports and integration with other information systems.

Vernon, Lynn R.↗
DOE OSTI2026

Transportation Secure Data Center: Frequently Asked Questions for Data Owners/Contributors

The Transportation Secure Data Center is a centralized repository for detailed transportation data from travel and transit surveys and studies conducted across the nation. It makes vital transportation data broadly available to users while preserving the privacy of survey participants. Hundreds of datasets from surveys and studies of household travel and transit passenger travel are archived in the TSDC, including surveys and studies conducted by state departments of transportation, metropolitan planning organizations, transit agencies, cities, and other public agencies. Detailed data from travel surveys and studies are extremely valuable for research purposes. However, the fine-grained information they contain could potentially be misused to identify individual travelers, so access to these data should only be granted with safeguards in place to protect participant privacy. The TSDC was created to address this challenge and to relieve public agencies from the burden of archiving their data and responding to data requests.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Transportation Secure Data Center

The Transportation Secure Data Center (TSDC) provides free access to detailed transportation data from a variety of travel surveys and studies conducted across the nation. Data include global positioning system (GPS) readings for millions of miles of travel, along with vehicle characteristics and survey participant demographics. 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.

Array↗
DOE OSTI2023

High-Reliability Systems and the Control of National Security Data and Information

High-reliability systems are characterized by catastrophic implications in the event of failure. These implications can include substantive damage to the environment, social order, and loss of life. Examples of high-reliability systems include nuclear submarines, nuclear reactors, the electric grid, and nuclear weapons. Due to the catastrophic implications of failure, there are heightened awareness and control mechanisms surrounding related data and information. However, defining the difference between data and information is often ambiguous across scholarly disciplines and in United States policy and legislation. For high-reliability systems, the implications of ambiguity between data and information may affect the security of United States interests and even cost lives. For security, data are raw facts or figures without context, while information is the compilation or articulation of data that forms context. Security depends on clarity in the differences between data and information and how to control them. Control is necessary to ensure that data and information are not unintentionally released to foreign governments, the public, or those without need-to-know. A primary concern in the practice of security is the control of data to avoid the unintended conversion to information. Intra-institutionally, this control is highly complex given the amalgam of legacy data systems and the numerous and constantly evolving nature of modern data systems that were not necessarily designed to be integrated. The complexity of this concern is augmented when institutions are part of interinstitutional collaborations or networks of public-private partnerships that share data and information. Additionally, institutions that share data as a function of policy and legislative action— particularly formally integrated data and information system infrastructures—may be at higher security risk. This paper will present an intra-institutional paradigm that utilizes and integrates concepts from numerous disciplines to frame a critical and underspecified practical issue in security—controlling for the unintended conversion of data to information.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

High-Reliability Systems and the Control of National Security Data and Information

High-reliability systems are characterized by catastrophic implications in the event of failure. These implications can include substantive damage to the environment, social order, and loss of life. Examples of high-reliability systems include nuclear submarines, nuclear reactors, the electric grid, and nuclear weapons. Due to the catastrophic implications of failure, there are heightened awareness and control mechanisms surrounding related data and information. However, defining the difference between data and information is often ambiguous across scholarly disciplines and in United States policy and legislation. For high-reliability systems, the implications of ambiguity between data and information may affect the security of United States interests and even cost lives. For security, data are raw facts or figures without context, while information is the compilation or articulation of data that forms context. Security depends on clarity in the differences between data and information and how to control them. Control is necessary to ensure that data and information are not unintentionally released to foreign governments, the public, or those without need-to-know. A primary concern in the practice of security is the control of data to avoid the unintended conversion to information. Intra-institutionally, this control is highly complex given the amalgam of legacy data systems and the numerous and constantly evolving nature of modern data systems that were not necessarily designed to be integrated. The complexity of this concern is augmented when institutions are part of inter-institutional collaborations or networks of public-private partnerships that share data and information. Additionally, institutions that share data as a function of policy and legislative action—particularly formally integrated data and information system infrastructures—may be at higher security risk. This paper will present an intra-institutional paradigm that utilizes and integrates concepts from numerous disciplines to frame a critical and underspecified practical issue in security—controlling for the unintended conversion of data to information.

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

Transportation Secure Data Center: Real-World Data for Planning, Modeling, and Analysis

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.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

TSDC: Transportation Secure Data Center: Real-World Data for Planning, Modeling, and Analysis

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.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Secure Data Logging and Processing with Blockchain and Machine Learning (Final Report)

Secure Data Logging and Processing with Blockchain and Machine Learning (ML) research is focused on the development of a platform to securely log and process sensor data in fossil power plants. The platform integrates two emerging technologies, blockchain and ML, and incorporates several innovative mechanisms to ensure the integrity, reliability, and resiliency of power systems. The goal is to protect the power plant from various cyberattacks such as false data injection and denial of service attacks using these technologies. The research goal was enabled by the following Research Project Objectives: 1) Secure authentication and identity verification of sensor nodes, actuators, and other equipment within a network. 2) Development of mechanisms that ensure only data sent by legitimate sensors are accepted and stored in the data repository. 3) Development of data aggregation methodologies using ML / Deep Learning (DL) algorithms to minimize noise / faulty data. 4) Implementation of the blockchain technologies to provide data security using secured IOTA framework & nodes.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRSDate not supplied

Simulating Secure Data Exchange and Storage for Urban Air Mobility Environments

Urban Air Mobility (UAM) defines an environment for managing operations of vertical takeoff and landing (VTOL) and short takeoff and landing (STOL) vehicles in an urban environment. Within a UAM environment, UAM operators manage fleets of vehicles, relying on Providers of Services for UAM (PSUs) for managing flights in a region of airspace. Flight plan deconfliction is primarily performed by the Discovery and Synchronization Service (DSS), and the Federal Aviation Administration (FAA) maintains control over the UAM space via the FAA-Industry Exchange Protocol (FIDXP). UAM is a federated environment with many different entities owning and operating vehicles, PSUs, and other services. These entities often need to interoperate or access data generated by other organizations. This paper demonstrates the feasibility of using blockchain to facilitate a secure data exchange and storage for this flight information in a UAM environment. In particular, this paper is focused on flight plans and telemetry data. A blockchain network was developed with a set of smart contracts for managing relevant flight data. Hyperledger Fabric was chosen as it is performent, scalable, and allows organizations to reuse existing public key infrastructure (PKI) for identity management. A set of simulated UAM services were also developed. These services propose flight plans and negotiate with other UAM services for airspace access. All interactions between UAM services, as well as vehicle telemetry data, is recorded onto the blockchain. Vehicle telemetry data is generated by a vehicle flight simulation service. This paper successfully demonstrates the feasibility of using blockchain as a secure data exchange and storage mechanism in a UAM environment.

UAM↗
NASA NTRSDate not supplied

Immutable Secure Data Exchange and Storage for UAM Environments

Urban Air Mobility (UAM) has become a focus for the next generation of aerial passenger transportation. UAM operations will be leveraging a service-based architecture for airspace solutions. The UAM environment will leverage diverse communications and system access approaches. These approaches include independent Providers of Services for UAM and supplemental data service providers that exchange data between themselves and UAM operators. This research focuses on the secure data exchange and storage of this decentralized UAM environment to address these challenges. This research intends to leverage a permissioned blockchain approach to address cybersecurity threats that may impact a UAM environment.

Urban Air Mobility↗
NASA NTRSDate not supplied

Immutable Secure Data Exchange and Storage for Urban Air Mobility Environments

Urban air mobility (UAM) is a concept that proposes to develop short-range aerial vehicles to overcome increasing surface congestion. Within the UAM environment, UAM operators work collaboratively to manage aerial vehicles in the urban environment. Providers of Services for UAM (PSU), UAM operators, and Supplemental Data Service Providers (SDSP) provide services to support flight operations within the UAM environment. The growth in the development of UAM systems, and the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. To address these challenges, this research focuses on the secure data exchange and storage of this decentralized UAM environment. The intent of this research is to leverage a permissioned blockchain approach to address cybersecurity threats that may impact a UAM environment.

Urban Air Mobility↗
NASA NTRSDate not supplied

Immutable Secure Data Exchange and Storage for Urban Air Mobility Environments

Urban air mobility (UAM) is a concept that proposes to develop short-range aerial vehicles to overcome increasing surface congestion. Within the UAM environment, UAM operators work collaboratively to manage aerial vehicles in the urban environment. Providers of Services for UAM (PSU), UAM operators, and Supplemental Data Service Providers (SDSP) provide services to support flight operations within the UAM environment. The growth in the development of UAM systems, and the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. To address these challenges, this research focuses on the secure data exchange and storage of this decentralized UAM environment. The intent of this research is to leverage a permissioned blockchain approach to address cybersecurity threats that may impact a UAM environment.

Urban Air Mobility↗