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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Threat Hunt Guide for BESS Environments

The rapid digitalization of the electric grid - driven by the integration of inverter-based resources (IBRs), battery energy storage systems (BESS), and advanced grid control platforms - has significantly enhanced grid efficiency, visibility, and flexibility. However, this evolution also introduces new cybersecurity risks, particularly through supply chain dependencies and operational blind spots at the grid edge. To address these challenges, Idaho National Laboratory (INL), through the Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response (CESER) Rapid Risk initiative, conducted a series of rapid risk assessment engagements with energy organizations across the United States. Drawing on lessons learned from these engagements, INL developed the following threat hunting guide for asset owners and operators (AOOs) to enhance their cybersecurity visibility within BESS and IBR systems. The guide demonstrates how to use passive network monitoring to baseline device behavior, detect adversarial activity, and investigate anomalies without disrupting operations. By implementing these practices, energy sector stakeholders can improve coordination between cybersecurity and operations teams and strengthen the resilience of distributed energy resources (DERs) within the modern power grid. Prior to implementing any network monitoring, packet capture, or threat hunting activity described in this guide, AOOs are strongly advised to review applicable governance frameworks, legal requirements, and organizational policies. This guide is intended for informational and educational purposes only. It does not replace compliance with any federal, state, or local cybersecurity mandates or industry standards. Implementation of described configurations, technologies, or analytic workflows is performed at the discretion and responsibility of the asset owner and operator.

25 - ENERGY STORAGE↗

DOE OSTI2024

Cybersecurity Considerations and Research Pathways for Grid-Interactive Efficient Buildings

Federal facilities serve critical missions and functions that require safe, reliable, and efficient operations. Digitization of several facility operations has increased the cost-effectiveness of energy usage and optimization of energy system performance. As the building controls landscape shifts to become more connected and smarter, building operators now face unique opportunities and challenges to adopt smart enabled devices that can lower energy usage while also optimizing building system performance. The grid-interactive efficient buildings (GEB) initiative aims to make buildings cleaner and more flexible through these smart devices. Smart enabled devices allow greater connectivity and control through remote operations and provide crucial data for analytics and increased efficiency. GEBs enable demand flexibility that has the potential to reduce electrical costs and transform the grid edge where buildings connect to power grids. This operation of interconnected systems, if not designed with cybersecurity practices, causes security gaps and introduces potential attack paths by adversarial and non-adversarial entities leading to disruption of operations.

building controls↗

DOE OSTI2022

A Hybrid Data-Driven and Model-Based Anomaly Detection Scheme for DER Operation

This paper proposes a hybrid data and model-based anomaly detection scheme to secure the operation of distributed energy resources (DERs) in distribution grids. Data-driven autoencoders are set up at the edge device level and they use local DER operational data as inputs. The abnormal statuses are detected by analyzing reconstruction errors. In parallel, modelbased state estimation (SE) is set up at the central level and it uses system-wide models and measurements as data inputs. The anomalies are identified by analyzing measurement residuals. The hybrid scheme preserves the benefits of both data-driven and model-based analyses and thus improves the robustness and the accuracy of anomaly detection. Numerical tests based on the model of a real distribution feeder in Southern California highlight the proposed scheme's effectiveness and benefits.

anomaly detection↗

DOE OSTI2026

Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels (Final Scientific/Technical Report)

The project, "Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels," represents a significant advancement in nuclear fuel recycling technology. It integrates cutting-edge multimodal sensor fusion, machine learning (ML), and digital twin (DT) technologies to address challenges in material safeguarding, process optimization, and regulatory compliance for pyroprocessing facilities. This research has significantly enhanced the understanding of pyrochemical fuel recycling processes by developing innovative tools and methodologies. The Multimodal Safeguards Monitoring Unit (MSMU) combines electroanalytical techniques, Raman spectroscopy, and differential thermal analysis (DTA) to enable high-fidelity, near-real-time material accountancy measurements. Machine learning techniques, such as Long Short-Term Memory (LSTM) autoencoders, are utilized to detect anomalies in material balances and sensor data, improving the reliability of safeguards monitoring. Additionally, digital twin technology has been established to provide real-time system-level monitoring and diagnostics, integrating physics-based models with sensor data to optimize process safety and efficiency.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2020

Research Report: Building a Wide Reach Corpus for Secure Parser Development

Whether developing from a specification or deriving parsers from samples, LangSec parser developers require widereach corpora of their target file format in order to identify key edge cases or common deviations from the format’s specification. In this work-in-progress paper, we report the details of several methods we’ve used to gather 30 million files, extract features and make these features amenable to search and other analytics. This paper documents opportunities and limitations of some popular open source data and tools and this paper will benefit researchers who need to efficiently gather a large file corpus.

Timmaraju, Virisha↗

DOE OSTI2024

Cyber Resilience and Social Equity: Twin Pillars of a Sustainable Energy Future

This paper examines the intersection of security and accessibility within energy systems amidst the rise of grid modernization and digitization, especially considering the regulatory changes and the imperatives of inclusive energy strategies. It addresses the dual need for secure, resilient infrastructure and a commitment to mitigate energy poverty while maintaining equitable access to energy. Amid escalating cybersecurity and physical threats, the paper advocates for sustainable energy delivery systems that ensure robust defenses without compromising the goals of reducing energy poverty and ensuring energy security. This paper identifies the pressing need for Cyber-Informed Engineering (CIE) and Secure-by-Design (SbD) principles, highlighting how these strategies can protect critical infrastructure and democratize access to secure energy, particularly for disadvantaged communities. The analysis underscores the challenges presented by the expansion of attack surfaces, interoperability requirements, and grid-edge analytics, offering innovative solutions that leverage advanced technologies and data-driven insights. Furthermore, this paper addresses the workforce development gap, emphasizing the necessity for public-private partnerships and vendor engagement in creating a skilled cybersecurity workforce. This paper has a dual focus on both the technological aspect of cybersecurity and the social dimension of equity within the context of sustainable energy development. It suggests a comprehensive examination of how these two critical elements interact and support the overarching goal of a sustainable energy future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Hyper Spectral Anomaly Detection

Anomaly detection is a common machine learning (ML) task with growing importance in the fields of imaging, quality assurance, and multiple security related disciplines. Anomaly detection is more difficult than traditional machine learning methods due to the inherent unlabeled nature of the datasets. Existing anomaly detection architectures commonly face challenges with explainability, retaining information related to the relational structure of the data, and false positive rates. Hyperspectral Imaging Anomaly Detection (HSI) is a statistical model that employs vertex and edge weighted graphs to preserve the data’s relationships on different topographical scales. The model is able to generalize from anomaly detection in 2D images to novel datasets related to cyber-security. Furthermore, the use of multi-spectral and other filtering methods results in fewer false positives and increases the explainability of model predictions. When applying HSI to cyber-security datasets, we are able to successfully detect malicious activity with a relatively high degree of accuracy.

97 - MATHEMATICS AND COMPUTING↗

NASA NTRS2016

AFSPC Innovation and Science and Technology Outreach to Industry and Academia

The U.S. Air Force is taking a strategic approach to ensuring that we are at the cutting edge of science and technology. This includes fostering game-changing approaches and technologies that are balanced with operational needs. The security of the Nation requires a constant pursuit of science, technical agility, and a rapid adoption of innovation. This includes pursuits of game-changing technologies and domains that perhaps we cannot even imagine today. This paper highlights the Air Force Space Command (AFSPC) collaboration and outreach to other government agencies, military and national laboratories, industry, and academia on long term science and technology challenges. In particular we discuss the development of the AFSPC Long Term Science and Technology Challenges that include both space and cyberspace operations within a multi-domain environment and the subsequent Innovation Summits.

Science Technology S&T Forum↗

DOE OSTI2022

A Hybrid Data-Driven and Model-Based Anomaly Detection Scheme for DER Operation: Preprint

This paper proposes a hybrid data and model-based anomaly detection for securing the operation of distributed energy resources (DERs) in distribution grids. Data-driven autoencoders (AE) are set up at the edge level by taking local DER data and detect anomalous operations by leveraging the reconstruction ability. In parallel, model-based state estimation (SE) is running at the system level by taking system models and measurements, the anomalies are identified by analyzing the measurements residual. The hybrid scheme preserves the benefits of both data-driven and model-based analysis and thus improves the robustness and accuracy of anomaly detection. It can be established by getting full use of the existing infrastructures in distribution grids. Numerical tests on a realistic distribution feeder in Southern California highlight the effectiveness as well as benefits of the proposed scheme.

anomaly detection↗

DOE OSTI2022

US Department of Energy, Office of Science High Performance Computing Facility Operational Assessment 2021: Oak Ridge Leadership Computing Facility

Oak Ridge National Laboratory’s (ORNL’s) Leadership Computing Facility (OLCF) continues to surpass its operational target goals of supporting users; delivering fast, reliable computational ecosystems; creating innovative solutions for high-performance computing (HPC) needs; contributing to the community to build the next generation HPC workforce, and managing risks, safety, and security associated with operating some of the most powerful computers in the world. The results can be seen in the cutting-edge science conducted by users and the praise from the research community. Calendar year (CY) 2021 saw continued excellence in research supported by the OLCF’s leadership-class computing resources, including Summit (the nation’s most powerful supercomputer), the global scratch file system Alpine, the Scalable Protected Infrastructure (SPI), the Exploratory Visualization Environment for Research in Science and Technology (EVEREST), and the archival mass-storage resource High-Performance Storage System (HPSS). While maintaining access and exceptional user support for Summit, the OLCF continued to make progress on the installation and deployment of Frontier, which will be the nation’s first exascale system when it comes online at the start of CY 2023. Users have already begun running and optimizing scientific codes on Crusher, the OLCF test and development system equipped with Frontier’s architecture. Throughout the year, the OLCF maintained a strong culture of operational excellence, including risk management, workplace safety, and cybersecurity. The OLCF’s rigorous risk management strategy anticipated and mitigated risks, and at this time there are no high-priority operational risks. Similarly, ORNL and the OLCF were committed to operating under the US Department of Energy’s (DOE’s) safety regulations that ensure a safe workplace. Technical staff tracked and monitored existing threats and vulnerabilities within the OLCF while continually developing tools and practices to enhance operations without increasing the facility’s risk. CY 2021 was filled with outstanding results and accomplishments, including a very high rating from users on overall satisfaction for the eighth consecutive year; a tremendous number of node hours delivered to 1,671 researchers on Summit; and the successful delivery of the allocation split of roughly 60%, 20%, and 20% of core-hours offered for the Innovative and Novel Computational Impact on Theory and Experiment (INCITE), Advanced Scientific Computing Research Leadership Computing Challenge (ALCC), and Director’s Discretionary (DD) programs, respectively (Section 2). COVID-19 research remained a focus in 2021, and the ALCC and DD programs allocated over 1 million Summit hours to the COVID-19 High Performance Computing Consortium. These accomplishments, coupled with the high utilization rates (i.e., overall and capability usage), represent the fulfillment of the promise of leadership class machines: efficient facilitation of leadership-class computational applications.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2000

Manual adjustable probe tool for friction stir welding

A friction stir welding tool is provided generally comprising three parts: a rotatable welding tool body (22) that has an outer threaded surface (32) and a probe (24) extending from a distal end of the body, a shoulder (26), which has a threaded inner surface (40) and a bore (36) at a distal end of the shoulder, and a jam nut (28), which has a threaded inner surface (42). The shoulder is threaded onto the tool body such that the probe extends from the shoulder through the bore by a preferred length. The jam nut is then threaded onto the tool body to secure the shoulder. The tool is operatively connected to a drive motor for rotating the tool body. The shoulder may include a knife edge projecting from the distal end (38) thereof adjacent the bore. The knife edge inhibits the weld material from migrating along the probe to intrude inside the shoulder, where it may prevent separation of the tool body and the shoulder when readjustment of the tool is necessary.

Oelgoetz, Peter A.↗

DOE OSTI2025

TRIM: AI Guided Random Number Generation for Resource-Constrained IoT Systems

Random numbers often serve as the backbone for many security solutions in diverse domains such as cryptography, side channel leakage prevention, and moving target defense. However, generating true random numbers requires a physical source of entropy (e.g. hardware, quantum, environmental phenomenon) making it difficult to realize at a large scale and at a low cost. On the flip side, pseudorandom number generators (easy to implement) following a specific distribution (e.g. Gaussian) can be easily compromised given a sufficient amount of traces. In this work, we have developed a machine learning-guided generative approach that can be used to create portable, resource-efficient, and cost-effective random number generators with high throughput and true randomness characteristics. We implement the proposed approach as a highly parameterized framework and perform extensive evaluation for different settings. The framework was able to learn from true random sources such as irrational numbers and environmental audio noise and imitate those sources towards generating new good quality random numbers on demand. We have generated more than 1 billion bits and observed robust performance in terms of true randomness metrics obtained from NIST SP 800-22 and FIPS 140-1 randomness test suites achieving a throughput of up to 142.85 Mbps. Compared to the state-of-the-art (SOTA) technique, the iso-cost setup of our framework can achieve more than 500 Mbps in a distributed setting. We have evaluated the efficacy of running the true randomness imitation AI models on target edge devices such as Raspberry Pi 4 (Model B), Nvidia Jetson Nano, Nvidia Jetson Orin Nano and Nvidia Jetson Xavier. We have also looked at the security of the TRIM framework itself against different adversarial threat models.

Cybersecurity↗

NASA NTRS2004

Middleware and Web Services for the Collaborative Information Portal of NASA's Mars Exploration Rovers Mission

We describe the design and deployment of the middleware for the Collaborative Information Portal (CIP), a mission critical J2EE application developed for NASA's 2003 Mars Exploration Rover mission. CIP enabled mission personnel to access data and images sent back from Mars, staff and event schedules, broadcast messages and clocks displaying various Earth and Mars time zones. We developed the CIP middleware in less than two years time usins cutting-edge technologies, including EJBs, servlets, JDBC, JNDI and JMS. The middleware was designed as a collection of independent, hot-deployable web services, providing secure access to back end file systems and databases. Throughout the middleware we enabled crosscutting capabilities such as runtime service configuration, security, logging and remote monitoring. This paper presents our approach to mitigating the challenges we faced, concluding with a review of the lessons we learned from this project and noting what we'd do differently and why.

Sinderson, Elias↗

NASA NTRS2012

Methods for Improving Long-Range Wireless Communication Between Extreme Terrain Vehicles

Axel is an extreme terrain, two-wheeled rover designed to traverse rocky surface and sub-surface landscapes in order to conduct remote science experiments in hard-to-reach locations. The rover's design meets many requirements for a mobile research platform capable of reaching water seeps on Martian cliff sides. Axel was developed by the Mobility and Robotic Systems section at the Caltech Jet Propulsion Laboratory. Unique design criteria associated with extreme terrain mobility led to a unique rover solution, consisting of a central module, which provides long-term energy storage and space for large-scale science payloads, and two detachable Axels that can detach and explore extreme terrain locations that are inaccessible to conventional rovers. The envisioned mission could involve a four-wheeled configuration of Axel called 'DuAxel' that is able to traverse the benign, flattened terrain of a landing site and approach the edge of the targeted crater or cave where it would deploy anchoring legs and detach one of the Axel rovers [1]. A tether provides a secure link between the Axel rover and the central module, acting as an anchor to allow Axel to descend along steep crater walls to collect data from the scientifically relevant sites along the water seeps or crater ledges. After completing its scientific mission Axel would hoist itself up to the central module and dock autonomously (using its on-board stereo cameras), allowing the once-again recombined DuAxel to travel to another location to repeat data collection.

wireless communication↗

NASA NTRS1993

Tool For Installation Of Seal In Tube Fitting

Plierslike tool helps secure repair seal in fitting. Tool crimps repair seal into tube fitting, ensuring tight fit every time. Modified pair of snapring pliers to which knife-edge jaws have been added. Spring added between handles. Also includes separate, accompanying support ring.

Trevathan, Joseph R.↗

NASA NTRSDate not supplied

Data & Reasoning Fabric

A Data & Reasoning Fabric shall provide a set of secured software infrastructure, tools, protocols, governance and policies to implement, administer, manage and operate data sharing and reasoning services across the entire span of air mobility and other ”smart” edge nodes.

Aeronautics↗

NASA NTRSDate not supplied

Data & Reasoning Fabric Overview

National Airspace↗

DOE OSTI2023

Enabling Advanced Energy Systems for Clean, Secure, and Sustainable Economic Growth Across Asia

The U.S. Agency for International Development (USAID) has partnered with the U.S. Department of Energy's national laboratories to form the Advanced Energy Partnership for Asia. Led by USAID and the National Renewable Energy Laboratory (NREL), the Partnership supports energy sector transformation and the growth of clean, secure, and sustainable energy markets across Asia.

Advanced Energy Partnership for Asia↗