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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Detection and Diagnosis of Data Integrity Attacks in Solar Farms Based on Multilayer Long Short-Term Memory Network

Photovoltaic (PV) systems are becoming more vulnerable to cyber threats. In response to this emerging concern, developing cyber-secure power electronics converters has received increased attention from the IEEE Power Electronics Society that recently launched a cyber-physical-security initiative. Here this letter proposes a deep sequence learning based diagnosis solution for data integrity attacks on PV systems in smart grids, including dc–dc and dc–ac converters. The multilayer long short-term memory networks are used to leverage time-series electric waveform data from current and voltage sensors in PV systems. The proposed method has been evaluated in a PV smart grid benchmark model with extensive quantitative analysis. As a comparison, we have evaluated classic data-driven methods, including K-nearest neighbor, decision tree, support vector machine, artificial neural network, and convolutional neural network. Comparison results verify performances of the proposed method for detection and diagnosis of various data integrity attacks on PV systems.

42 ENGINEERING↗
DOE OSTI2020

Anomaly Detection and Mitigation for Wide-Area Damping Control using Machine Learning

In an interconnected multi-area power system, wide-area measurement based damping controllers are used to damp out inter-area oscillations, which jeopardize grid stability and constrain the power flows below to their transmission capacity. The effect of wide-area damping control (WADC) significantly depends on both power and cyber systems. At the cyber system layer, an adversary can inflict the WADC process by compromising either measurement signals, control signals or both. Stealthy and coordinated cyber-attacks may bypass the conventional cybersecurity measures to disrupt the seamless operation of WADC. This paper proposes an anomaly detection (AD) algorithm using supervised Machine Learning and a model-based logic for mitigation. The proposed AD algorithm considers measurement signals (input of WADC) and control signals (output of WADC) as input to evaluate the type of activity such as normal, perturbation (small or large signal faults), attack and perturbation-and-attack. Upon anomaly detection, the mitigation module tunes the WADC signal and sets the control status mode as either wide-area mode or local mode. The proposed anomaly detection and mitigation (ADM) module works inline with the WADC at the control center for attack detection on both measurement and control signals and eliminates the need for ADMs at the geographically distributed actuators. Here, we consider coordinated and primitive data-integrity attack vectors such as pulse, ramp, relay-trip and replay attacks. The performance of the proposed ADM algorithms was evaluated under these attack vector scenarios on a testbed environment for 2-area 4-machine power system. The ADM module shows effective performance with 96:5% accuracy to detect anomalies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Visualizing Comparisons of Bill of Materials

Protecting critical infrastructure from cyber attacks, natural disasters, and other disruptions is a priority of the U.S. Government. Critical infrastructure includes providing electricity to homes and businesses, supplying natural gas for heating, and producing renewable energy sources. A loss of these services, as seen in the Solarwinds supply chain attack in 2020 , Texas snowstorm of 2021, the Colonial Pipeline cyber incident of 2021, and the Washington power substation attacks in 2022 result in high costs to consumers, disruption of everyday life, and even death. To protect the infrastructure, we first have to know what equipment we are protecting. The complexity of distributed manufacturing and development coupled with the increasing prevalence of cyber and supply chain attacks necessitates a greater understanding of the hardware and software components that comprise equipment in critical infrastructure. When a vulnerability in a single software library can have disastrous consequences, it is vital to understand critical equipment and systems at a granular level. This need has led to increased energy around the development and incorporation of bill-of-materials (BOM) into existing asset management practices to aid in mitigating, and responding to future attacks \cite{noauthor_software_nodate}. While much of the current research is devoted to creating BOMs, it is equally important to develop methodologies for leveraging BOMs to answer questions, such as: How has my software changed? Are two pieces of equipment equivalent? Does this piece of equipment that just arrived match my historical information? In this work, we demonstrate how BOMs can be represented by graph structures. We then describe how these structures can be fed into a graph comparison algorithm to produce a novel interactive visualization that allows us to not only identify differences in BOMs, but show exactly where they are in the product.

Jones, Rebecca D.↗
DOE OSTI2026

Engineering Controls Database

Cyber-Informed Engineering (CIE) addresses the reality that cyber attacks on engineered systems can have consequences far beyond data loss or disruption of digital networks. When control systems are compromised, safety, reliability, and performance of the physical process itself may be threatened. This database is meant to establish clear examples and guidance for defining and applying engineered controls in CIE. It explains what engineered controls are, how they differ from information security measures, and how they are integrated into system design. The goal is to ensure that resilience is engineered into systems from the outset. Unlike cybersecurity protections that defend the digital layer, engineered controls act directly at the physical and algorithmic levels to guarantee that unacceptable consequences are prevented or limited. CIE keeps the consequences of a cyber attack from impacting the safety, reliability, and performance of engineered systems.

Source record↗
DOE OSTI2022

DER Cybersecurity Detection and Response Suite

SAND2024-08475O The Distributed Energy Resource (DER) Cybersecurity Detection and Response Suite is a solution for distributed energy resource (DER) systems. The DER Security Orchestration, Automation, and Response (SOAR) solution that uses alerts from signature- and behavior-based Intrusion Detection Systems are intended to be deployed as bump-in-the-wire (BITW) devices in front of DER equipment. The fielded application would use multiple intrusion detection systems that report data to SOAR to respond to cyberattacks. The suite consists of two software components: • The proactive intrusion detection and mitigation system (PIDMS) secures grid-edge photovoltaic smart inverters and other equipment in distributed energy resource systems. It is a distributed BITW solution; cyber and physical data are automatically processed using network inspection tools and custom machine learning algorithms to detect abnormal events and correlate cyber-physical events. • The Security Orchestration, Automation, and Response for Distributed Energy Resources (SOAR4DER) application ingests data from several intrusion detection systems to quickly block attacks and revert DER systems to good states. Using a collection of intrusion detection system technologies on a BITW device, it incorporates physical and cyber data to detect abnormal and potential malicious behaviors. Multiple SOAR playbooks then use the intrusion detection system data streams to automatically defend the system. SOAR4DER system testing showed detection and response times under 30 seconds for all adversary reconnaissance, denial-of-service attacks, malicious Modbus commands, brute-force logins, and machine-in-the-middle attacks. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Johnson, Jay↗
DOE OSTI2020

Collaboration with USACyS for Signals Manipulation

Savannah River National Laboratory has been investing into cybersecurity capabilities since 2019. Cybersecurity has been a rapidly growing area for the CSRA in general and has included the consolidation of Cyber and Electronic Warfare units at Fort Gordon. This consolidation has been driven by the convergence of cyberwarfare and electronic warfare as the technologies underpinning Army Cyber Command’s vision of Information Warfare. This convergence, investment by SRNL into cybersecurity for critical infrastructure, and the desire for collaboration between the Army and SRNL has resulted in an opportunity for SRNL to develop R&D capabilities in electronic warfare that will complement SRNL’s cybersecurity efforts. Discussions between SRNL and the U.S. Army Cyber School (USACyS) has identified a general gap in ICS training lab capability in which both entities could contribute to solve. It is proposed to design and architecture that meets this need.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Tensor Text-Mining Methods for Malware Identification and Detection, Malware Dynamics Characterization, and Hosts Ranking

Malware is one of the most persistent and costly cyber threats endangering reputation, confidentiality, integrity, and availability for organizations and national security. Consequently, many of the incident detection and prevention systems, and incident responders have begun to utilize machine learning as a helper in the fight against malware and other cyber threats. However, cyber defenders rely on interpretability and generalizability, yet the popular machine learning methods are black-box and often use traditional supervised solutions that do not generalize to novel malware. Therefore, there is a need to improve the existing solutions. At the same time, the majority of the prior research ignored essential evaluation criteria when reporting the results of their methods, which disables the safe reproducibility of the methods in a production environment. Tensor decomposition, on the other hand, enables interpretable unsupervised analysis of the large-scale data for the discovery of hidden patterns. Our findings, performed on real-world and large-scale experiments, show that tensor factorization-based methods yield performance results that surpasses or competes with existing supervised solutions with the added benefit of interpretability and generalizability. With the ability to analyze complex and large-scale data using tensors, we report results that reflect real-world production environments. We propose to develop new game- changing tools for malware identification and characterization that can trace malware evolution, rank the infected or malicious hosts, and streamline the work of incident response teams, malware analysts, and incident detection and prevention systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Securing Inverter Communication: Proactive Intrusion Detection and Mitigation System to Tap, Analyze, and Act

The electric grid has undergone rapid, revolutionary changes in recent years; from the addition of advanced smart technologies to the growing penetration of distributed energy resources (DERs) to increased interconnectivity and communications. However, these added communications, access interfaces, and third-party software to enable autonomous control schemes and interconnectivity also expand the attack surface of the grid. To address the gap of DER cybersecurity and secure the grid-edge to motivate a holistic, defense-in-depth approach, a proactive intrusion detection and mitigation system (PIDMS) device was developed to secure PV smart inverter communications. The PIDMS was developed as a distributed, flexible bump-in-the-wire (BITW) solution for protecting PV smart inverter communications. Both cyber (network traffic) and physical (power system measurements) are processed using network intrusion monitoring tools and custom machinelearning algorithms for deep packet analysis and cyber-physical event correlation. The PIDMS not only detects abnormal events but also deploys mitigations to limit or eliminate system impact; the PIDMS communicates with peer PIDMSs at different locations using the MQTT protocol for increased situational awareness and alerting. The details of the PIDMS methodology and prototype development are detailed in this report as well as the evaluation results within a cyber-physical emulation environment and subsequent industry feedback.

14 SOLAR ENERGY↗
DOE OSTI2021

Cooperative Research and Development Agreement among National Energy Technology Laboratory, The Board of Regents of the University System of Georgia by and on behalf of Georgia Institute of Technology, and University of Texas at El Paso [Abstract]

Georgia Institute of Technology (GIT), University of Texas at El Paso (UTEP), and the National Energy Technology Laboratory (NETL) will collaborate and develop cyber-physical systems for advanced power systems. GIT will test virtual models on hybrid power system hardware and cyber physical components. UTEP will test novel actuators on hybrid power system hardware. NETL will access GIT’s expertise in the engineering and real time simulation of power system processes for cyber-physical systems and simulations to control hardware. NETL will establish a relationship to promote UTEP’s capabilities in additive manufacturing and high-temperature piezo-electric devices with the potential for controlling high-temperature flows with a light and inexpensive device at high response rates. This project is the result of an EERE Advanced Manufacturing Office open Multi-Topic awarded proposal led by GIT titled, “Robust Combined Heat and Hybrid Power (CHHP) for High Electrical Efficiency Cogeneration”.

42 ENGINEERING↗
DOE OSTI2022

Smart Microgrids

The Nation’s electrical power depends on one bulk power grid to support security and economic prosperity. According to the Department of Homeland Security’s Homeland Threat Assessment of 2020, the largest cyber threat to homeland security is potential disruption to critical infrastructure, including power grids. Critical infrastructure includes the physical and cyber systems which generate, transmit, and distribute electricity with an impact on economic security, public health, or safety. The surety of the Nation’s power grid is vital for providing essential services and would put the population at risk if disrupted. Power outages can have catastrophic consequences for critical organizations such as hospitals and military installations. Additionally, the current fossil-fuel dependent power grid is extremely fragile and vulnerable to overloads, storms that destroy power lines, and cyber-attacks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Project: Corbomite - Product: ConsoleWorks REACT

TDi Technologies presents ConsoleWorks REACT, an advanced platform designed to tackle the complexities of cyber and operational risk assessment. This comprehensive solution goes beyond asset-focused approaches by considering the impact of both assets and people have on the security and operation of critical infrastructure, specifically targeting preventing gird mis-operation by evaluating real-time human interaction or commands with critical assets. The hypothesis suggests that by integrating the assessment of user commands into the overall risk assessment process, organizations can make more informed decisions, prioritize resources effectively, and respond promptly to potential threats. This hypothesis forms the basis for the development of a proactive and holistic risk management approach that is more comprehensive and context aware than traditional models which only look at assets, patch levels, configuration and threat intel by collecting that information off the network vs directly from the asset, human, and human interaction all in real-time. ConsoleWorks' unique man-in-the-middle architecture is a key feature that sets it apart in the cybersecurity landscape. This architecture enables the real-time observation, enforcement, and commands or interaction risk transparency of user interaction with critical infrastructure to be risk mitigated and audited as they are aggregated with device and human risk factors for a more comprehensive risk threat score across a device or group of devices. This architecture allows ConsoleWorks to act as a secure intermediary between users and critical assets, monitoring all interactions and ensuring that only authorized commands are sent to the asset to be executed. This not only enhances security but also provides a comprehensive audit trail of all user activities, contributing to compliance efforts and facilitating incident investigation and risk management. By integrating this unique architecture with our comprehensive risk assessment methodology, ConsoleWorks REACT provides a powerful solution for managing cyber and operational risks, enabling organizations to maintain a robust security posture and effectively mitigate potential threats. To that end, the primary objective of this project was to research and develop a robust solution that enables the energy industry to mitigate the risks associated with human actions that can compromise the security or operations of assets critical to energy delivery, generation, transmission and operation. ConsoleWorks REACT plays a pivotal role in achieving this goal by leveraging its unique capabilities to monitor and track all user activity. Through its Zero Trust approach, which emphasizes continuous verification, the platform ensures secure access to assets and serves as the centralized human response and notification platform for addressing cyber and operational issues.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Complete Evaluation on Advanced Reactor Machine Learning Subversion Attacks (Final)

Navigating through the world of Artificial Intelligence (AI) in nuclear reactors and their Instrumentation and Control (I&C) systems demands a careful, deliberate journey. AI’s capability to manage massive datasets and streamline control systems has indeed carved out a significant role in various sectors, including nuclear energy. However, while AI, and particularly Large Language Models (LLMs), bring a lot to the table in terms of operational efficiency and anomaly detection, they also expose the sector to a new breed of cybersecurity threats, like Inference Attacks, Adversarial Attacks, and Trojan Attacks. This guide is designed to be a straightforward manual, diving deep into the intertwining worlds of AI and cybersecurity within nuclear reactors, and tailoring insights for three crucial audiences: I&C Vendors/Developers, Nuclear Regulators, and Nuclear Reactor Operators and Cyber Defense Teams. (1) Section 2, directed at I&C Vendors/Developers, will provide a clear and focused look at several cybersecurity attacks, offering practical recommendations and detailed scenarios related to AI cybersecurity. This section isn’t just about identifying problems but also about giving solid, usable solutions. (2) Section 3, meant for Nuclear Regulators, gets straight to the point about regulations, policy suggestions, and guidelines that are needed to lay down a robust, secure, and ethical foundation for the application of AI in nuclear operations. The focus is on making sure that everything adheres to international standards and laws while being practicable and clear-cut. (3) Section 4, aimed at Nuclear Reactor Operators and Cyber Defense Teams, offers an exhaustive exploration and technical reports, with clear recommendations and scenario analyses vital to protect operational environments and guarantee the secure application of AI in nuclear reactor operations. The goal is simple: as we step into an era where AI becomes a fundamental element of our technological and energy infrastructures, this guide is here to act as a clear, direct handbook, ensuring that AI is implemented within the nuclear sector in a manner that is secure, responsible, and practical. It’s about striking a balance – optimizing the undeniable benefits offered by AI while securing and shielding against potential cyber threats as we move through this new and complex landscape.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Introduction to ALERT: A User-Interface Tool for Resilient Optimization

As the cyber-physical systems grow in complexity, there is a need for proactive resilience strategies that involve online, adaptive control actions to best prepare for any impending adversarial events. In this technical effort, supported by RD2C LDRD initiative, the project team designed and demonstrated online strategies – referred to as ALERT controls – for proactive and adaptive tuning of existing optimal controls in a microgrid, with quantifiably assured margins of resilience to various cyber-physical adversarial events. This ALERT functionality is made available to the end-users, e.g., the system operators, via an interactive user-interface. The end-users will not only be able to use the interface to visualize the system’s operation under various cyber-physical adversarial scenarios, but also evaluate the amount of tolerance the system has against selected adversarial perturbations of interest (e.g., malfunctioning sensors, suspected attacked measurements) via the adversarial plots. In this technical report, we briefly outline the algorithmic modules of the developed ALERT control technology, and introduce the user-interface tool that allows end-users (e.g., microgrid operators) to enter their system description, specify various operational and resilience requirements, and evaluate the impact of the control decisions via illustrative plots.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Living-off-the-land Techniques Unlikely to Supplant Energy Sector-Focused OT-Specific Malware

Despite increased reports of energy sector-focused threat actors using living-off-the-land (LOTL) techniques, it is unlikely LOTL techniques will wholly supplant malware in energy sector operational technology (OT)-focused cyber operations. Threat actors leverage LOTL techniques to access energy sector networks, abstracting process information and maintaining persistence. Although threat actors using LOTL techniques have successfully interrupted energy sector industrial control environments, designed features of OT-specific malware likely increase the cyber-physical impact of an attack and delay recovery of critical functions and services. Malicious actors will very likely continue to use LOTL techniques for stealth, while designing malware to bolster final impacts on cyber-physical systems in energy sector OT environments.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Utility-Scale Operational Consequences for Solar Grid Services

This report delves into the critical aspects of grid services provided by solar inverter-based resources (IBRs), with an emphasis on the evolving landscape of microgrids, virtual power plants (VPPs), aggregators, and distributed energy resource management systems (DERMS). As the energy sector undergoes a transformative shift towards more decentralized and resilient grid architectures, understanding the multifaceted risks associated with these technologies becomes paramount. The report categorizes these risks into organizational, technical, and procedural domains, providing a thorough risk assessment framework that stakeholders can utilize to anticipate and mitigate potential issues. In addressing the increasing complexity of grid interconnections, the report highlights the importance of Cyber-Informed Engineering (CIE). By embedding engineering controls and cybersecurity measures into the early stages of system design, this approach aims to fortify grid infrastructure against emerging cyber threats. The analysis includes an exploration of best practices and strategies for integrating CIE principles to enhance grid security and resilience. To provide practical insights, the report conducts a detailed consequence analysis of various grid services and cyber mitigations that can be applied through the interconnection process. This analysis evaluates the potential impacts of different failure modes and vulnerabilities, offering a clear understanding of the consequences that could arise from disruptions within the energy grid. The findings are further enriched by a series of case studies that illustrate real-world scenarios and lessons learned from past incidents. Through this comprehensive examination of grid services and their criticality, the report aims to prepare industry professionals with the knowledge and tools necessary to navigate the complexities of modern energy systems. By providing a comprehensive approach that includes risk assessment, cybersecurity, and consequence analysis, solar stakeholders can more effectively guarantee the reliability, efficiency, and security of the energy grid.

14 SOLAR ENERGY↗
DOE OSTI2025

Cybersecurity Incident Response Guide for Wind

As wind energy systems become increasingly digitized and interconnected, they face a growing array of cyber threats that can disrupt operations, compromise safety, and trigger cascading impacts across the energy ecosystem. The Wind Incident Response Guide provides a structured, wind-specific framework for preparing for, detecting, responding to, and recovering from cyber incidents. Drawing on lessons from field demonstrations, cyber-physical testbeds, and stakeholder engagement across the wind sector, this guide integrates technical, operational, and regulatory considerations to support asset owners, operators, and responders. It outlines key roles and responsibilities, maps incident response phases to wind-specific scenarios, and highlights applicable laws, regulations, standards, and best practices. By tailoring general cybersecurity principles to the unique architectures and operational constraints of wind systems—including remote access, legacy components, and environmental interfaces—this guide aims to enhance resilience, reduce response time, and support coordinated action across public and private stakeholders. It is intended as a practical resource for utilities, developers, regulators, and emergency managers working to secure the future of wind energy.

17 - WIND ENERGY↗
DOE OSTI2026

Cybersecurity Center for Offshore Wind Energy (Final Project Report)

This project establishes a Cybersecurity Center for Offshore Wind Energy with the objective of designing and operating a cyber-physical testbed for wind energy farms (WEFs) that enables comprehensive cybersecurity research. The testbed incorporates a Supervisory Control and Data Acquisition (SCADA) system connected to turbine models via industrial-grade programmable logic controllers (PLCs) and remote terminal units (RTUs). It supports side-channel data acquisition, implementation and analysis of various cyberattack scenarios, and development of attack detection, mitigation, and best-practice guidance tailored to wind energy systems. During the project, the team expanded the number and fidelity of mathematical turbine models (MTMs), integrated these models with SCADA infrastructure, and deployed a scaled physical turbine and associated sensors. High-resolution operational and side-channel data streams were collected and used to refine machine-learning (ML)-based attack detection systems and to extend the WindCRAFT framework to multi-turbine threat scenarios. The project demonstrated a realistic, scalable environment for evaluating cyber threats, validated attack detection approaches using enriched datasets, and identified new multi-turbine and inter-turbine communication attack vectors. The resulting testbed, models, and security mechanisms provide a foundation for ongoing R&D and deployment of cyber-resilient offshore wind energy systems.

17 WIND ENERGY↗
DOE OSTI2021

Vind: A Blockchain-Enabled Supply Chain Provenance Framework for Energy Delivery Systems

Enterprise-level energy delivery systems (EDSs) depend on different software or hardware vendors to achieve operational efficiency. Critical components of these systems are typically manufactured and integrated by overseas suppliers, which expands the attack surface to adversaries with additional opportunities to infiltrate into EDSs. Due to this reason, the risk management of the EDS supply chain is crucial to ensure that we are knowledgeable about the vulnerabilities in software and hardware components that comprise any critical part, quantifiable risk metrics to assess the severity and exploitability of the attack, and provide remediation solutions that can influence a prioritized mitigation plan. There is a need to realize cyber supply chain risk management for industrial control systems’ hardware, software, and computing and networking services associated with bulk electric system (BES) operations. This article proposes a blockchain-based cyber supply chain provenance platform (“Vind”) for EDSs to realize data provenance in a cyber supply chain ecosystem.

Bandara, Eranga↗