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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Comprehensive, Multi-Source Cyber-Security Events Data Set

This data set represents 58 consecutive days of de-identified event data collected from five sources within Los Alamos National Laboratory’s corporate, internal computer network. The data sources include Windows-based authentication events from both individual computers and centralized Active Directory domain controller servers; process start and stop events from individual Windows computers; Domain Name Service (DNS) lookups as collected on internal DNS servers; network flow data as collected on at several key router locations; and a set of well-defined red teaming events that present bad behavior within the 58 days. In total, the data set is approximately 12 gigabytes compressed across the five data elements and presents 1,648,275,307 events in total for 12,425 users, 17,684 computers, and 62,974 processes. Specific users that are well known system related (SYSTEM, Local Service) were not de-identified though any well-known administrators account were still de-identified. In the network flow data, well-known ports (e.g. 80, 443, etc) were not de-identified. All other users, computers, process, ports, times, and other details were de-identified as a unified set across all the data elements (e.g. U1 is the same U1 in all of the data). The specific timeframe used is not disclosed for security purposes. In addition, no data that allows association outside of LANL’s network is included. All data starts with a time epoch of 1 using a time resolution of 1 second. In the authentication data, failed authentication events are only included for users that had a successful authentication event somewhere within the data set.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Data transmission by quantum matter wave modulation

Abstract Classical communication schemes exploiting wave modulation are the basis of our information era. Quantum information techniques with photons enable future secure data transfer in the dawn of decoding quantum computers. Here we demonstrate that also matter waves can be applied for secure data transfer. Our technique allows the transmission of a message by a quantum modulation of coherent electrons in a biprism interferometer. The data is encoded in the superposition state by a Wien filter introducing a longitudinal shift between separated matter wave packets. The transmission receiver is a delay line detector performing a dynamic contrast analysis of the fringe pattern. Our method relies on the Aharonov–Bohm effect but does not shift the phase. It is demonstrated that an eavesdropping attack will terminate the data transfer by disturbing the quantum state and introducing decoherence. Furthermore, we discuss the security limitations of the scheme due to the multi-particle aspect and propose the implementation of a key distribution protocol that can prevent active eavesdropping.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

STUCCO

STUCCO will be a scalable visual analytics solution that identifies and collects endogenous and exogenous data from data sources not traditionally associated with cyber security to enable both the identification of relevant, non-security data with cyber security event data and the display of an overview of global security threats.

Goodall, John↗
DOE OSTI2018

Comparative Performance Evaluation of High-performance Data Transfer Tools

Data transfer in wide-area networks has been long studied in different contexts, from data sharing among data centers to online access to scientific data. Many software tools and platforms have been developed to facilitate easy, reliable, fast, and secure data transfer over wide area networks, such as GridFTP, FDT, bbcp, mdtmFTP, and XDD. However, few studies have shown the full capabilities of existing data transfer tools from the perspective of whether such tools have fully adopted state-of-the-art techniques through meticulous comparative evaluations. In this paper, we evaluate the performance of the four highperformance data transfer tools (GridFTP, FDT, mdtmFTP, and XDD) in various environments. Our evaluation suggests that each tool has strengths and weaknesses. FDT and GridFTP perform consistently in diverse environments. XDD and mdtmFTP show improved performance in limited environments and datasets during our evaluation. Unlike other studies on data transfer tools, we also evaluate the predictability of the tools' performance, an important factor for scheduling different stages of science workflows. Performance predictability also helps in (auto)tuning the configurable parameters of the data transfer tool. We apply statistical learning techniques such as linear/polynomial regression, and k-nearest neighbors (kNN), to assess the performance predictability of each tool using its control parameters. Our results show that we can achieve good prediction performance for GridFTP and mdtmFTP using linear regression and kNN, respectively.

Nadig, Deepak↗
DOE OSTI2024

Privacy by Design in Distributed Edge Systems: Innovating Secure Workflows for Smart Cities

The proliferation of distributed edge systems, such as those in smart cities, healthcare, and industrial IoT, offers unprecedented opportunities for data processing closer to its source, thereby reducing latency and enhancing efficiency. However, these systems also present significant privacy challenges due to the handling of sensitive data from multiple sources. This article explores the critical need for designing privacy-preserving workflows in distributed edge systems to ensure data security while maximizing the potential of edge computing. By examining the challenges, technological advancements, and potential of privacy-by-design approaches, we highlight the importance of integrating advanced privacy-preserving techniques like federated learning, differential privacy, homomorphic encryption, secure multi-party computation, and zero-knowledge proofs. These innovations are crucial for enhancing data security, regulatory compliance, and public trust in smart city applications, ultimately leading to safer and more efficient urban environments.

Kotevska, Olivera↗
DOE OSTI2019

Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions

Big data has a potential to unlock novel groundbreaking opportunities in the power grid sector that enhances a multitude of technical, social, and economic gains. The currently untapped potential of applying the science of big data for better planning and operation of the power grid is a very challenging task and needs significant efforts all-around. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data sets from diverse sources. In particular, computational complexity, data security, and operational integration of big data into utility decision frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. Moreover, due to the complex nature of power grids along with the need to balance power in real time, seamless integration of big data into utility operations is very critical. In this context, big data analytics combined with grid visualization can lead to better situational awareness and predictive decisions. This paper presents a comprehensive state-of-the-art review of big data analytics and its applications in power grids, and also identifies challenges and opportunities from utility, industry, and research perspectives. The paper analyzes research gaps and presents insights on future research directions to integrate big data analytics into electric utility decision framework. Detailed information for utilities looking to apply big data analytics and details insights on how utilities can enhance revenue streams and bring disruptive innovation in the industry are discussed. More importantly, general guidelines for utilities to make the right investment in the adoption of big data analytics by unveiling interdependencies among critical infrastructures and operations are also provided.

13 HYDRO ENERGY↗
DOE OSTI2018

Sequence Hopping Algorithm For Securing Goose Messages

Embodiments of the present invention include methods and apparatuses for securing data transfer in power-system automation and smart grids. Embodiments of the present invention can be particularly helpful in securing GOOSE messages. A method of securing data transfer according to an embodiment of the present invention can include a message sequence synchronization and monitoring server (MSSMS) providing random seeds to a publisher intelligent electronic device (IED) and a subscriber IED, both having a pseudo random number generators (PRNGs), inputting the random seeds into the PRNG of the publisher IED and generating a publisher sequence hopping number, sending a message to the subscriber IED with the publisher sequence hopping number attached, and determining if the message is valid by comparing the message attached publisher sequence hopping number to a subscriber sequence hopping number generated by the subscriber PRNG.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Methods for safely sharing dual-use genetic data

Background: Some genetic data has dual-use potential. Sharing pathogen data has shown tremendous value. For example therapeutic development and lineage tracking during the COVID pandemic. This data sharing is complicated by the fact that these data have the potential to be used for harm. The genome sequence of a pathogen can be used to enable malicious genetic engineering approaches or to recreate the pathogen from synthetic DNA. Standard data security methods can be applied to genetic data, but when data is shared between institutions, ensuring appropriate security can be difficult. Sensitive data that is shared internationally among a wide array of institutions can be especially difficult to control. Methods for securely storing and sharing genetic data with potential for dual-use are needed to mitigate this potential harm.Results: Here we propose new methods that allow genetic data to be shared in a data format that prevents a nefarious actor from accessing sensitive aspects of the data. Our methods obfuscate raw sequence data by pooling reads from different samples. This approach can ensure that data is secure while stored and during electronic transfer. We demonstrate that by pooling raw sequence data from multiple samples of the same organism, the ability to fully reconstruct any individual sample is prevented. In the pooled data, most genomic information remains, but reads or mutations cannot be directly attributed to any individual sample. To further restrict access to information, regions of a genome can be removed from the reads.Conclusion: Our methods obscure genomic information within raw sequence reads. This method can allow genetic data to be stored and shared while preventing a nefarious actor from being able to perfectly reconstruct an organism. Broad-scale sequence information remains, while fine scale details about specific samples are difficult or impossible to reconstruct. Our software is available at https://github.com/Geneinfosec-Inc/ReadMixer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

An Intelligent Distributed Ledger Construction Algorithm for IoT

Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to well-known security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain's linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work proposed a machine-learning approach with a multi-arm bandit that resolved these issues and was designed for auditing on limited devices. This algorithm was tested in a reinforcement-learning environment simulating the IOTA ledger's construction with a decision tree. This study showed through regret analysis and experimentation that this approach was secure against impulse manipulation attacks while remaining energy-efficient. Although the IOTA protocol was a pioneer for lightweight distributed ledgers, it is expected that future blockchain protocols will adopt techniques similar to those presented in this work.

multi-arm bandit↗
DOE OSTI2023

Blockchain Empowered Provenance Framework for Sensor Identity Management and Data Flow Security in Fossil-based Power Plants

The overall goals of this project are divided into 3 important phases. First, a peer-to-peer SCADA network is to be established where identity profile of sensors from fossil-power plants will be stored in an immutable manner in order to verify data flow integrity in real time. Second, it is aimed to develop a Blockchain-based provenance platform to audit equipment operations along with data and process integrity violations. In this phase, various operational rules will be incorporated in the ledger on behalf of the operating sensor so its abnormal use can be easily detected. In addition, it is aimed to achieve provenance when the rate of transactions is exceedingly high, thus enabling to design a scalable provenance scheme that can work in real time when number of data collecting points or sensor units increase in fossil-based power plants. Finally, a working testbed will be developed to integrate the Blockchain platform with sensor-based fossil power plant comprising of Remote Terminal Units (RTU) and SCADA devices. Using multiple Raspberry-PIs mimicking the role of RTUs that are interfaced with multiple sensors devices will be deployed to serve as a critical component of fossil power plant and the performance metrics such as throughput, latency, bootstrapping time, etc., will be evaluated on the testbed.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2018

Design Considerations for Secure Wireless Sensor Communication Systems in Harsh Electromagnetic Environments of Nuclear Reactor Facilities

Wireless sensors can potentially play a significant role in safety, efficiency, and reliability of the instrumentation and control process in current and next generation nuclear power reactors. While conventional narrowband wireless sensors have shown a certain level of success in some nuclear power plants (NPPs), the radio frequency (RF) propagation challenges posed by the heavy metallic and cluttered environment of NPPs has prevented their widespread use in such operations. Furthermore, these challenges include RF wave propagation in harsh (reflective, absorptive, cluttered) environments, data security issues, and RF interference to and from other devices in the vicinity of a nuclear reactor core. In this paper, first we address how ultrawideband (UWB) RF technology can complement the narrowband (i.e., WiFi) solutions that have been used in some NPPs by providing an alternative solution in addressing the signal propagation issues in such electromagnetically harsh environments. Second, we discuss and present the UWB software simulation results on multipath harsh environments, and then address the data security issues. In the final sections of the paper, we present the experimental results of using UWB signaling in a representative harsh environment conducted at the Massachusetts Institute of Technology research reactor site. We plan to develop the UWB communications hardware based on the results of this paper and report on its performance in the field with emphasis on the security aspects of the system in a subsequent paper.

42 ENGINEERING↗
NASA NTRS2014

Securing Ground Data System Applications for Space Operations

The increasing prevalence and sophistication of cyber attacks has prompted the Multimission Ground Systems and Services (MGSS) Program Office at Jet Propulsion Laboratory (JPL) to initiate the Common Access Manager (CAM) effort to protect software applications used in Ground Data Systems (GDSs) at JPL and other NASA Centers. The CAM software provides centralized services and software components used by GDS subsystems to meet access control requirements and ensure data integrity, confidentiality, and availability. In this paper we describe the CAM software; examples of its integration with spacecraft commanding software applications and an information management service; and measurements of its performance and reliability.

Security↗
DOE OSTI2025

Scale-up Unlearnable Examples Learning with High-performance Computing

Recent advancements in AI models, like ChatGPT, are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for future AI training without explicit consent, spotlighting critical privacy and intellectual property concerns around healthcare data usage. Addressing these privacy challenges, a novel approach known as Unlearnable Examples (UEs) has been introduced, aiming to make data unlearnable to deep learning models. A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources (e.g., a single workstation). To push the boundaries of UE performance with theoretically unlimited resources, we scaled up UC learning across various datasets using Distributed Data Parallel (DDP) training on the Summit supercomputer. Our goal was to examine UE efficacy at high-performance computing (HPC) levels to prevent unauthorized learning and enhance data security, particularly exploring the impact of batch size on UE’s unlearnability. Utilizing the robust computational capabilities of the Summit, extensive experiments were conducted on diverse datasets such as Pets, MedMNist, Flowers, and Flowers102. Our findings reveal that both overly large and overly small batch sizes can lead to performance instability and affect accuracy. However, the relationship between batch size and unlearnability varied across datasets, highlighting the necessity for tailored batch size strategies to achieve optimal data protection. The use of Summit’s high-performance GPUs, along with the efficiency of the DDP framework, facilitated rapid updates of model parameters and consistent training across nodes. Our results underscore the critical role of selecting appropriate batch sizes based on the specific characteristics of each dataset to prevent learning and ensure data security in deep learning applications. The source code is publicly available at https: // github. com/ hrlblab/ UE_ HPC .

Zhu, Yanfan [Vanderbilt University, Nashville, TN,↗
DOE OSTI2024

GraphCH: A Deep Framework for Assessing Cyber-Human Aspects in Insider Threat Detection

Insider threat is one of the most damaging cyber attacks that could cause the loss of intellectual property and enterprise data security breaches. Action sequence data such as host logs are used to investigate such threats and develop anomaly-based AI detectors. However, insider threat actions are similar to legitimate user activities, causing AI detectors to fail and suffer from high false alarm rates. Therefore, user cyber activity logs are inadequate to fully unfold insider threats. In this study, we adopt human psychological principles of risk-taking and impulsiveness along with host data to assess the influence and usefulness of human behavioral aspects in insider threat detection. Here, we hypothesize that individuals' impulsive and risk-taking behavior correlates with cyberspace activities. To validate our hypothesis, we conducted an IRB-approved study recruiting 35 participants who work in a large U.S. university and collected their cyber and psychological data for 90 days. Host and human-behavioral data analysis and mapping indicate that impulsive and risk-taking users trigger more system errors causing (un)intentional insider threats and are susceptible to attackers' social engineering and cognitive hacking. Utilizing cyber-human aspects, we introduce a Cyber-Human Graph Neural Network (GNN) based framework GraphCH to identify abnormal user behaviors and detect insider threats.

97 MATHEMATICS AND COMPUTING↗