Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Cyber”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Cyber Labeling for Energy Industrial IoT

The U.S. Department of Energy’s (DOE) Office of Cybersecurity, Energy Security and Emergency Response (CESER), at the request of the Deputy National Security Advisor for Cyber and Emerging Technologies, Anne Neuberger, initiated research in 2023 to develop a cybersecurity labeling proof-of-concept for energy products to expand on the Federal Communications Commission’s (FCC) proposed U.S. Cyber Trust Mark program. DOE mobilized researchers from six National Laboratories to develop and gather feedback on a proof-of concept label for solar inverters and smart meters, which serve as representative products for market-facing energy sector Industrial Internet of Things (IIoT). This report details the research team’s process across two phases and the resulting findings, which include challenges facing cyber labeling programs and recommendations to implement an expanded IIoT cyber labeling program in the U.S.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2022

Cyber-Resilient Distributed Autonomous Energy Grid

The aim of Cyber-Resilient Distributed Autonomous Grid research initiative is to advance fundamental science and engineering approaches for cyber-resilient design, control, and operation of a distributed, highly interconnected, and autonomous energy grid of the future. From increasing penetration of DERs, smart homes and building with highly controllable loads at the distribution layer, to the interconnection of bulk renewables at the transmission layer the fundamental nature of the energy grid is changing, and these changes are enabled by rapid increase in dependence on the communication infrastructure and independent third parties for control and operation of the grid. NREL's Integrated Energy Pathways critical objective correctly identifies that there are fundamental cyber-resilience challenges inherent in the grid's evolution, and this effort is establishing strong and novel integrated cyber-resilience framework and approaches to address these new and fundamental challenges.

controls↗
DOE OSTI2022

Cyber–physical vulnerability and resiliency analysis for DER integration: A review, challenges and research needs

High penetration of renewable and sustainable Distributed Energy Resources (DER) into the traditional distribution system requires a well-coordinated control strategy for the improvement of system-wide reliability and resiliency. Implementation of such a holistic control architecture requires a flexible, near real-time, and bi-directional communication framework for facilitating the participation of various agents in a multi-vendor heterogeneous smart grid. While the sustainability of energy generation is ensured, this exposes the smart grid to extrinsic cyber threats, and appropriate defense mechanism(s) must be deployed to guarantee continued reliability and resiliency of the power grid. Further, the comprehensive literature review presented in this paper discusses the latest trends in the DER control schemes with fast communication requirements and their accompanying cyber–physical vulnerabilities. These control schemes are compared and contrasted for various traits. A three-level DER system architecture has been depicted, facilitating the deployment of these control schemes. The current developments of standard communication protocols, key security mechanisms, and best practices along major standards and guidelines are explored. The impacts of different attack types with miscellaneous DER functions based on various control schemes and associated mitigation solutions are also provided. Finally, challenges and future research directions for limiting cyber-power susceptibility to enhance resiliency are summarized. The work presented here will help us enabling a cyber-resilient and sustainable smart electric grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Cyber-Physical Reconfiguration for Disaster Resilience of Power Distribution Systems

Cyber-physical distribution systems (CPDS) have emerged from the integration of information technology into distribution systems. While offering substantial benefits, this integration also introduces vulnerabilities. The interaction between cyber networks and distribution systems renders CPDS susceptible to disasters. To ensure critical load supply and system resilience, rapid post-disaster load restoration is required. The paper proposes a critical load restoration (CLR) framework in CPDS using a network reconfiguration approach that exploits the existing post-disaster resources to restore critical loads within the shortest possible time. Using graph theory, the cyber network and distribution system are integrated into a single digraph, minimizing the CLR complexity in CPDS. A cost metric is also defined to satisfy network-specific objectives and constraints. A heuristic is proposed to guide the load restoration process using the cost metric within the integrated digraph. Simulation results confirm the framework's superiority over existing literature, which either overlooks cyber components or prolongs restoration with additional resource deployment.

cyber-physical system↗
DOE OSTI2024

Rapid Monitoring and Defense Approach for Resilience Improvement of Grid Cyber Security

Cyber-physical systems and electric utilities significantly depend on the reliability and efficiency of information and operational technology. However, false data injection attacks based on synchrophasor measurement data pose a serious threat to the safe and reliable operation of modern power systems. Here, to mitigate this problem, a rapid monitoring and defense approach is proposed to defend against cyber attacks. Initially, the Time and Frequency based Convolutional neural Network (TFCN) is proposed to detect different types of attacks. Within the TFCN, the advances are that both time and frequency domain information can be fused without extra spectrum analysis methods, and can save detection time to speed the calculation efficiency using the developed time-frequency block. Next, a comprehensive defense strategy is developed for multiple cyber attacks to ensure the stability and resilience of the power system according to the feedback detection results. The advances of this strategy are that different control strategies can be automatically selected to recover the stability to the greatest extent according to the detected attacks. To verify the effectiveness of the proposed approach, the high-speed frequency measurements collected from the wide-area monitoring system are used. The results demonstrate that the cyber attack detection performance can reach 95.57% accuracy, outperforming both traditional and some advanced neural networks. Importantly, the defense strategy is conducted and verified in a modified IEEE 39 bus system as well, which illustrates profound performance in faster stability restoration.

Comprehensive defense strategy↗
DOE OSTI2022

WAMS-Based HVDC Damping Control for Cyber Attack Defense

Owing to the fast and large power regulating the capacity of the HVDC system, the wide-area measurement system (WAMS) based high voltage direct current (HVDC) system has been regarded as a prospective solution to deal with the low-frequency oscillation issue. However, due to the vulnerability of the WAMS communication, WAMS based HVDC system control can be a prime target of malicious penetrations that could lead to disastrous events. To remediate this adverse effect, an improved WAMS based HVDC damping control framework is proposed in this paper. First, a lightweight network named Attack Shuffle convolutional neural Networks (ASNet) is proposed to learn the characteristics of cyber attacks. Then, a model-free-based cyber attack defense framework is introduced to quickly identify the attack types based on the continuous wavelet transform and ASNet. Additionally, an improved control framework of the WAMS and HVDC-based wide-area power oscillation damping control (WH-PODC) is developed to provide different response control for mitigation of the impact of cyber attacks. Finally, the performance of the proposed WH-PODC control framework is evaluated with real PMU data in multiple scenarios in RTDS, where the results indicate that the response intensity can be kept under multiple types of cyber attacks while providing similar effectiveness in oscillation suppression to conventional controls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Data-Driven Cyber-Attack Detection for PV Farms via Time-Frequency Domain Features

The internetworking of grid-connected power electronics converters (PECs) in photovoltaic (PV) farms has inevitably expanded the cyber-attack surfaces. Here this paper presents a comprehensive study on cyber-attack detection and diagnosis for PEC-enabled PV farms via single waveform sensor to distinguish between normal conditions, open-circuit faults, short-circuit faults, and cyber-attacks. To our knowledge, this has not been attempted before. Firstly, we propose frequency-domain magnitude-based residuals to identify short-circuit faults and a time-domain mean current vector-based feature to distinguish open-circuit faults from other threats. These features can fully reflect the specific physical characteristics of PV farms during threat duration. Secondly, unlike micro phasor measurement units (µPMU) and raw electric waveform-based methods, the proposed innovative features can address novel cyber-attacks that are excluded from the training process. Thirdly, an online hardware-in-the-loop (HIL) testbed using the OPAL-RT real-time digital simulator has verified the effectiveness. The monitoring system runs in real-time while using HIL as an operational solar farm and a National Instruments (NI) data acquisition card as the electric waveform sensor at the point of coupling.

42 ENGINEERING↗
DOE OSTI2022

Prioritizing ICS Beachhead Systems for Cyber Vulnerability Testing

Cyber Testing for Resilient Industrial Control Systems™ (CyTRICS™) is the Department of Energy’s (DOE’s) program for cybersecurity vulnerability testing, digital subcomponent enumeration, and forensic assessment. CyTRICS leverages best-in-class test facilities and analytic capabilities at six DOE National Laboratories and strategic partnerships with key stakeholders including technology developers, manufacturers, asset owners and operators, and interagency partners. During the program’s development, CyTRICS established a unique methodology for prioritizing digital components within operational technology (OT) and industrial control systems (ICS) in the Energy Sector Industrial Base (ESIB) for cyber vulnerability testing. The CyTRICS Prioritization Process leverages multiple characteristics of systems, components, and their contextual deployment to calculate a quantification of individual digital components for CyTRICS testing. The initial version of the CyTRICS Prioritization Process was premised largely upon the impact which could result to an industrial control system if the digital component under testing was compromised, either through malicious means, faulty engineering, or other modes. The worldwide compromise of the SolarWinds Orion platform, first reported in December 2020, through malicious interference with the digital patching cycle was a watershed event in cyber supply chain security. The SolarWinds compromised demonstrated the strategic importance of certain types of ubiquitous software, and the ability to generate widespread cybersecurity effects. To address this challenge and as a part of the Department of Energy’s response to the SolarWinds compromise, DOE’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER) directed the National Laboratories to evolve the CyTRICS Prioritization Process methodology to encompass additional factors related to the strategic importance of digital components. CESER directed CyTRICS researchers to identify, characterize, and append strategic factors to the CyTRICS Prioritization Process to provide additional weight to these characteristics. National Laboratory expert researchers identified functionality, distribution, and platform characteristics for digital components in ICS and OT that they assessed would be likely targeted in strategic initial-access cyber attack. CyTRICS has termed these factors “ICS Beachhead Systems,” leveraging a definition first advanced by Schneider Electric, which is intended as a blanket term to encompass digital components, products, and systems in OT. This paper describes the ICS Beachhead Systems identified and the rationale for inclusion. As a next step in the research and refinement process, the National Laboratories will validate this initial set of characteristics against digital components evaluated by the CyTRICS program and current implementation of the CyTRICS Prioritization Process. After validation, CyTRICS researchers will then develop a scoring methodology to generate a quantitative score to assess the degree to which a digital component is characterized as an ICS Beachhead System. Finally, the National Laboratories will append this scoring to the existing CyTRICS Prioritization Process algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Cyber Informed Engineering (CIE) Principles Slide Presentation [Slides]

This document describes the concept and application of Cyber-Informed Engineering (CIE), a methodology that integrates cyber threat awareness into all stages of the systems engineering life cycle. It delineates how CIE enhances the security posture of critical infrastructure systems, which are increasingly targeted by sophisticated cyber threats. The exposition proceeds to methodically walk through the twelve foundational principles of CIE, each serving as a strategic guidepost for embedding cybersecurity into the fabric of system design, development, operation, and maintenance. The principles highlight the importance of proactive and comprehensive security measures that span from risk assessment to continuous improvement, ensuring that systems are not only designed with security in mind but are also resilient in the face of evolving cyber threats.

42 ENGINEERING↗
DOE OSTI2022

Precursor Analysis Report: Cyber Attack on Thyssenkrupp Blast Furnace 2014

The Cyber Attack on Thyssenkrupp Blast Furnace 2014 Precursor Analysis Report leverages publicly available information about the Thyssenkrupp Steel Mill cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. In December 2014, the German Government’s Federal Office for Information Security (BSI) released a report detailing a cyber attack on a German steel mill that occurred earlier that year, though exact dates and details of the attack were not revealed. While the report did not specify the name of the company, multiple sources identified the victim as one of Europe’s largest steel manufacturers, Thyssenkrupp AG. Further, Thyssenkrupp announced on 16 May of that year that Europe’s largest blast furnace, “Schwelgern 2,” located at its facility in Duisburg, Germany, would be offline for several weeks for repairs and upgrades, suggesting Schwelgern 2 was likely the target of the attack. The attack began in early 2014, when adversaries infiltrated the victim steel mill’s Information Technology (IT) network via a spearphishing campaign, then worked their way into the Operational Technology (OT) environment, where they executed software that caused denial of service, denial of control, and eventually a loss of control. This led to the blast furnace shutting down without proper safety procedures, resulting in catastrophic physical damage. No lives were lost in the incident, but ThyssenKrupp suffered $4 million in damage to the blast furnace and an additional $6 million in lost revenue. The adversaries required specialized knowledge and expertise in steel production, which enabled them to compromise a variety of internal systems and components across both IT and OT networks. The attack also demonstrated detailed knowledge of the industrial control systems (ICS) and production processes being used. This combination resulted in one of the earliest known publicly reported cybersecurity incidents resulting in physical damage to ICS equipment. Researchers and analysts identified 19 unique techniques (used in a sequence of 20 steps) utilized during the attack with a total of 454 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Fifteen of the identified techniques used during the Thyssenkrupp cyber attack were precursors to the triggering event. Analysis identified 369 observables associated with these precursor techniques, 316 of which were assessed to have an increased likelihood of being perceived in the 120 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRSDate not supplied

Cyber Resiliency and the Implementation of a Host-Based Intrusion Detection System in an Urban Air Mobility Environment

With the growth in urban air mobility systems and the increasing reliance on interconnected technologies, ensuring the security of these complex infrastructures has become critical. As cities evolve into smart urban centers, the vulnerability to cyber threats escalates, possibly endangering citizens’ safety and the efficiency of transportation networks. In response to these challenges, this paper presents a study on implementing a Host-Based Intrusion Detection System (HIDS) tailored explicitly to urban mobility environments’ unique demands. This study explores the design, development, and deployment of the proposed HIDS, focusing on its adaptability to monitor the hybrid nature of the urban mobility environment. Leveraging machine learning algorithms and anomaly detection techniques, the HIDS is equipped to continuously monitor and analyze the behavior of individual host systems, vehicles, and devices, thereby providing a proactive approach to threat detection. Implementing a HIDS is a pivotal strategy for enhancing cyber resiliency, as it gives an organization granular visibility into internal system activities, enables rapid detection and response to anomalous behavior and cyber threats, and fortifies the organization’s overall cybersecurity posture. In conclusion, this paper highlights the significance of host-based intrusion detection in urban mobility environments and underscores the necessity of tailored security solutions to safeguard against emerging cyber threats.

UAM↗
DOE OSTI2025

Dynamic probabilistic risk assessment and game theory for cyber security risk analysis in nuclear power plants

Nuclear Power Plants and energy systems have become more prone to cyber-attacks with their digitalization and the increased use of smart equipment. Hence, it is important to quantify the risk associated with cyber-attacks in such systems. Dynamic Probabilistic Risk Assessment which involves studying the evolution of a system due to random events and operator and attacker actions during a cyber-attack by employing a physics-based model of the system is a suitable framework to quantify cybersecurity risk in nuclear power plants. In addition to the plant dynamics, it is also important to model the strategies of the attackers and plant operators for an effective cybersecurity risk assessment. Game theory provides a set of necessary tools to model such strategic interactions. In this research, a framework that integrates dynamic probabilistic risk assessment with game theory for cybersecurity risk analysis in nuclear power plants is presented. The mathematical formulation is derived based on the theory of continuous event trees. We propose a game theory based action model, that utilizes physics-based rewards to define the strategies of attackers and operators at every decision epoch. As a case study, the risk associated with cyber-attacks on the digital components in the secondary side of a pressurized water reactor is studied using a reduced order model. A set of attacker actions and a set of operator actions are defined for the system. The operator and attacker interactions were modelled using simultaneous game, their action policies were computed using the concept of mixed strategy Nash equilibrium and the evolution of the system was studied.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Computational Framework for Control Co-Design of Resilient Cyber–Physical Systems With Applications to Microgrids

Critical infrastructure networks, such as power and transportation networks, can be modelled as cyber-physical systems. As the complexity of such systems grow, there is need for developing metrics and design tools that will co-optimize the physical components of the system and the control policies to guarantee resilience against cyber and natural threats. To that end, we develop a simulation-based control co-design computational framework that will concurrently determine the system and control parameters of a cyber-physical system to meet pre-specified resilience, operational and economic objectives. Here, the capabilities of the developed co-design engine is demonstrated by designing the physical components and control parameters of a microgrid system that will meet its resiliency objectives when subjected to various cyber and physical threats.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Cy-Phy ADS: Cyber Physical Anomaly Detection Framework for EV Charging Systems

Today’s large-scale Electric Vehicle (EV) infrastructures are heavily dependent on information communication technologies to maintain their operation and to support communication within sub-system components as well as the outside world. These technologies are vulnerable to various cyber and physical threats. Timely identification and mitigation of these threats are critical for improving human safety, avoiding economic losses, and preventing catastrophic system failures. By addressing this, our work presents a ResNet Autoencoder (AE) based Cyber-Physical Anomaly Detection System (Cy-Phy ADS) for detecting anomalies in EV Controller Area Network (CAN) protocol communication. It consists of four main components: Cyber-Physical Feature Extractor, ResNet AE-based Anomaly Detection Framework, Cyber-Physical Health Metric (CPHM), and Visualization Dashboard. The presented framework was trained and tested using CAN data collected from the EV charging system testbed at the Idaho National Laboratory. The presented Cy-Phy ADS compared against six widely used unsupervised anomaly detection algorithms: One Class Support Vector Machine (OCSVM), Variational Autoencoder (VAE), LSTM Autoencoder (LSTM AE), Isolation Forest (IForest), Principle Component Analysis (PCA) and Local Outlier Factor (LOF). Here the presented approach showed the highest accuracy among the compared methods. Further, the proposed approach showed comparable performance in terms of precision, F1, and False positive rate. It also showed the lowest training and inference time compared to the neural network-based baseline algorithms compared against with. Additionally, the Cy-Phy ADS has advantages such as unsupervised training, the ability to provide a holistic metric for system health characterization, and non-linear feature extraction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Towards a High Fidelity Training Environment for Autonomous Cyber Defense Agents

Cyber defenders are overwhelmed by the frequency and scale of attacks against their networks. This problem will only be exacerbated as attackers leverage AI to automate their workflows. Autonomous cyber defense capabilities could aid defenders by automating operations and adapting dynamically to novel threats. However, existing training environments fall short in areas such as generalization, explainability, scalability, and transferability, making it intractable to train agents that will be effective in real networks. In this paper we take an important step towards creating autonomous cyber defense agents — we present a high fidelity training environment called Cyberwheel that includes both simulation and emulation capabilities. Cyberwheel simplifies customization of the training network and easily allows redefining the agent’s reward function, observation space, and action space to support rapid experimentation of novel approaches to agent design. It also provides visibility into agent behaviors necessary for agent evaluation and sufficient documentation / examples to lower the barrier to entry. As an example use case of Cyberwheel, we present initial results training an autonomous agent to deploy cyber deception strategies in simulation.

Oesch, T↗
DOE OSTI2023

DSS-SimPy-RL (Open-DSS and SimPy based Cyber-Physical RL environment) [SWR-23-29]

Recently, numerous data-driven approaches to control an electric grid using machine learning techniques have been investigated. With the advancement of reinforcement learning (RL) based techniques, gradually the conventional optimization based solvers are being replaced with RL approach where there is uncertainty in the environment such as renewable generation or cyber system emulation. However, to train an agent efficiently, it requires numerous interactions with an environment to learn the best policies. There are numerous RL environments for the power systems based on some well-known simulators, similarly there are environment for communication domains. While majority of the cyber emulators are based in an UNIX environment, the power simulators are based in the Windows-based operating system, the generation of cyber-physical mixed domain RL environment has been challenging. Existing co-simulation methods are efficient but resource and time intensive to generate large scale data set for training RL agents. Hence, this software focuses on development and validation of a mixed domain RL environment using Open DSS for the physical side and leverages a discrete event simulator python package, SimPy, for cyber-side emulation which is Operating Systems agnostic. Further utilizing this software co-simulation and training RL agents for re-routing based resilient control for network reconfiguration and volt-var control in power distribution feeder are performed.

Sahu, Abhijeet↗
DOE OSTI2024

Cyber-informed Engineering Microgrid Analysis Tool

The Cyber-Informed Engineering Microgrid Analysis Tool (CIEMAT) leverages the Department of Energy’s Cyber-Informed Engineering to prompt engineering designers and operators through an analysis of the critical functions to be supported by a microgrid installations, the criticality of those functions, the impacts of denial, disruption or misuse of those functions on the microgrid and dependent functions, and the mitigations which could best prevent impacts to those functions resulting from cyber attack. Through use of this tool, microgrid designers and operators can quickly identify appropriate engineering mitigations to limit impacts from cyber attack and functions where engineering and operational staff can prioritize and guide the application of cybersecurity protections to best support the resiliency of the system.

Wright, VirginiaL [Idaho National Laboratory (INL)↗
DOE OSTI2025

Cyber-informed Engineering Battery Analysis Tool

The Cyber-Informed Engineering Battery Analysis Tool (CIEBAT) leverages the Department of Energy’s Cyber-Informed Engineering to prompt engineering designers and operators through an analysis of the critical functions to be supported by a BESS installation, the criticality of those functions, the impacts of denial, disruption or misuse of those functions within the BESS system, and the mitigations which could best prevent impacts to those functions resulting from cyber attack. Through use of this tool, BESS designers and operators can quickly identify appropriate engineering mitigation opportunities to limit impacts from cyber attack and functions where engineering and operational staff can prioritize and guide the application of cybersecurity protections to best support the resiliency of the system.

Lampe, BenjaminR [Idaho National Laboratory (INL),↗