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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Design of a Novel Information System for Semi-Automated Management of Cybersecurity in Industrial Control Systems

There is an urgent need in many critical infrastructure sectors, including the energy sector, for attaining detailed insights into cybersecurity features and compliance with cybersecurity requirements related to their Operational Technology (OT) deployments. Frequent feature changes of OT devices interfere with this need, posing a great risk to customers. One effective way to address this challenge is via a semi-automated cyber-physical security assurance approach, which enables verification and validation of the OT device cybersecurity claims against actual capabilities, both pre- and post-deployment. To realize this approach, this paper presents new methodology and algorithms to automatically identify cybersecurity-related claims expressed in natural language form in ICS device documents. Here, we developed an identification process that employs natural language processing (NLP) techniques with the goal of semi-automated vetting of detected claims against their device implementation. We also present our novel NLP components for verifying feature claims against relevant cybersecurity requirements. The verification pipeline includes components such as automated vendor identification, device document curation, feature claim identification utilizing sentiment analysis for conflict resolution, and reporting of features that are claimed to be supported or indicated as unsupported. Our novel matching engine represents the first automated information system available in the cybersecurity domain that directly aids the generation of ICS compliance reports.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

Effects of Gain/Loss Framing in Cyber Defense Decision-Making

Cyber defense requires decision making under uncertainty. Yet this critical area has not been a strong focus of research in judgment and decision-making. Future defense systems, which will rely on software-defined networks and may employ ‘moving target’ defenses, will increasingly automate lower level detection and analysis, but will still require humans in the loop for higher level judgment. We studied the decision making process and outcomes of 17 experienced network defense professionals who worked through a set of realistic network defense scenarios. We manipulated gain versus loss framing in a cyber defense scenario, and found significant effects in one of two focal problems. Defenders that began with a network already in quarantine (gain framing) used a quarantine system more than those that did not (loss framing). We also found some difference in perceived workload and efficacy. Alternate explanations of these findings and implications for network defense are discussed.

cyber security↗
DOE OSTI2013

Integrated Assessment Plan Template and Operational Demonstration for SPIDERS Phase 2: Fort Carson

This document contains the Integrated Assessment Plan (IAP) for the Phase 2 Operational Demonstration (OD) of the Smart Power Infrastructure Demonstration for Energy Reliability (SPIDERS) Joint Capability Technology Demonstration (JCTD) project. SPIDERS will be conducted over a three year period with Phase 2 being conducted at Fort Carson, Colorado. This document includes the Operational Demonstration Execution Plan (ODEP) and the Operational Assessment Execution Plan (OAEP), as approved by the Operational Manager (OM) and the Integrated Management Team (IMT). The ODEP describes the process by which the OD is conducted and the OAEP describes the process by which the data collected from the OD is processed. The execution of the OD, in accordance with the ODEP and the subsequent execution of the OAEP, will generate the necessary data for the Quick Look Report (QLR) and the Utility Assessment Report (UAR). These reports will assess the ability of the SPIDERS JCTD to meet the four critical requirements listed in the Implementation Directive (ID).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Securing Single Points of Compromise (SPoC)

Securing the Single Points of Compromise that provide central services to the institution’s environment is paramount to success when trying to protect the business. (Fisk, 2014) Time Based Security mandates protection (erecting and ensuring effective controls) that last longer than the time to detect and react to a compromise. When enterprise protections fail, providing additional layered controls for these central services provides more time to detect and react. While guidance is readily available for securing the individual critical asset, protecting these assets as a group is not often discussed. Using best business practices to protect these resources as individual assets while leveraging holistic defenses for the group increases the opportunity to maximize protection time, allowing detection and reaction time for the SPoCs that is commensurate with the inherent risk of these centralized services.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Managing Risks Associated with Global Nuclear Energy Expansion: Emerging Challenges and Cooperative Solutions Workshop Summary Report.

Global nuclear energy has reached a critical juncture. The footprint of nuclear energy is growing and will continue to grow in coming decades to meet increasing global energy demands, desires for energy security, and mounting concerns about climate change. This growth includes construction of reactors in countries new to the nuclear energy enterprise, in addition to expansion of existing programs. The lack of operational experience coupled with weak regulatory systems in some countries raises the potential of a nuclear accident. The expansion of nuclear energy is also met with an increasingly complex threat environment, with threats to nuclear security from non-state actors as well as the continued risks of state proliferation. The trend towards increasingly digitized and networked nuclear facilities significantly expands operational uncertainty and adds complexity to implementing safeguards and security. These factors merit fresh consideration of potential safety, safeguards, security, and cyber (3SC) risks, as well as approaches for managing those risks in an integrated, sustainable, and internationally cooperative manner. In an effort to explore the emerging challenges and cooperative solutions to the global expansion of nuclear energy The George Washington University Elliott School of International Affairs and Sandia National Laboratories convened a group of more than thirty experts from government, national laboratories, non-government organizations, and academia on May 5th, 2016 at George Washington University to discuss these issues in a not-for-attribution environment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Next-Generation Performance-Based Regulation. Volume 1: Introduction - Global Lessons for Success; Volume 2: Primer - Essential Elements of Design and Implementation; Volume 3: Innovative Examples from Around the World

Performance-based regulations (PBRs) provide a regulatory framework to connect goals, targets, and measures to utility performance or executive compensation. Well-designed PBRs provide incentives for utility performance, benefiting consumers and utility owners alike. This report considers the role of both PBRs and more discrete performance incentive mechanisms (PIMs) in 21st century power sector transformation. Innovative technologies are transforming the way electricity is generated, delivered, and consumed. PBRs have the potential to realign utility, investor, and consumer incentives and mitigate emerging challenges to the utility business model, renewable integration, and even cyber security.The goals of PBRs in the form of multi-year rate plans are in many respects the same in terms of providing reasonably priced and reliable service to customers. However, today's technologies have changed, and there is more emphasis on clean energy. Thus, the pathways and the potential outcomes are different than they were in the 20th century when centralized generator stations and large infrastructure additions dominated the utility landscape. Given unprecedented changes underway in the electricity sector, PBRs - by specifying expectations of utility performance and outcomes for consumers, while staying agnostic to the exact means of delivery - constitute a form of prescient regulation that harnesses disruption. PBRs are one tool in a broader toolbox in the transition toward flexible regulatory and market structures that rewards utilities that adapt or evolve in reaction to market and technology change. PBRs and PIMs have great value for the electric industry when designed well and can be applied to many different situations. How exactly PBR mechanisms are most effectively enacted will vary based on the utility ownership model, institutional arrangements, and a variety of other local factors. PBRs should be tailored to the needs and goals of each jurisdiction, and perhaps each utility, to most effectively achieve the needs of a 21st century power grid in that jurisdiction. Presented in three volumes, this report highlights the lessons learned from their evolving history, explores essential elements of their design and implementation as well as considerations for how they may be best applied, and examines leading examples of PBRs from the United Kingdom, New York, Denmark, Mexico, and South Africa. The full report, 'Next-Generation Performance Based Regulation - Emphasizing Utility Performance to Unleash Power Sector Innovation,' published in September 2017, can be accessed at https://www.nrel.gov/docs/fy17osti/68512.pdf.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Distributed Energy Resource Cybersecurity Framework Best Practices

Current cybersecurity challenges for distributed energy resources (DERs) stem from the integration of various systems and the cyber-physical security concerns they bring. Although frameworks exist for industrial control systems and other energy systems, a simple guided tool that can prioritize recommended actions for controls specific to DERs is lacking. Additionally, the novelty of the field has made it difficult to create a standardized procedure for DERs with cybersecurity in mind. In response to this critical need to address cybersecurity risk management, the National Renewable Energy Laboratory (NREL) has developed a framework and accompanying web application known as the Distributed Energy Resources Cybersecurity Framework (DERCF) (Powell, 2019). A brief overview of the DERCF can be found in Section 1.2. This document provides a guide to cybersecurity best practices identified by technical research and site visits where the framework was used to assess the cybersecurity posture of DER systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Electrification Annual Progress Report (FY2019)

During fiscal year 2019 (FY 2019), the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) funded early stage research & development (R&D) projects that address Batteries and Electrification of the U.S. transportation sector. The VTO Electrification Sub-Program is composed of Electric Drive Technologies, and Grid Integration activities. The Electric Drive Technologies group conducts R&D projects that advance electric motors and power electronics technologies. The Grid and Charging Infrastructure group conducts R&D projects that advance grid modernization and electric vehicle charging technologies. This document presents a brief overview of the Electrification Sub-Program and progress reports for its R&D projects. Each of the progress reports provide a project overview and highlights of the technical results that were accomplished in FY 2019.

25 ENERGY STORAGE↗
DOE OSTI2023

Toward a Resilient Cybersecure Hydropower Fleet: Cybersecurity Landscape and Roadmap 2021

With this roadmap, Pacific Northwest National Laboratory (PNNL) hopes to assist the U.S. Department of Energy’s (DOE’s) Water Power Technologies Office (WPTO) in improving the cybersecurity of hydropower plants across the nation. This effort draws upon collected data from the dams sector, from industrial control system cybersecurity threat reports, from similar work focused on neighboring sectors, and from frank discussions with owners, operators, and vendors. While remaining tightly focused on the needs of hydropower projects, during this landscape study and development of the resulting roadmap, the research team sought to remain informed by the larger energy sector’s vision and direction so that the topics and milestones may fit within a larger vision common to the whole.

13 HYDRO ENERGY↗
DOE OSTI2023

Attack Surface Analysis of the Digital Twins interface with Advanced Sensor and Instrumentation Interfaces: Cyber Threat Assessment and Attack Demonstration for Digital Twins in Advanced Reactor Architectures

A digital twin is a virtual representation of a physical system or object using real-time data that can predict and analyze how the system or object performs. This relatively new technology can be applied to the field of nuclear power generation, to aid in the design and development of new nuclear power plants and reduce operation costs using predictive maintenance and other data analytical methods. While there are already companies utilizing simulation software to train operators and technicians in the nuclear industry, some are now transitioning to utilizing their existing technology, software, and methods to develop digital twin solutions for the next generation of nuclear power plants, offering their services to utilities and government organizations around the world.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Cybersecurity Considerations for the Liquified Natural Gas Sector

Due to the highly volatile nature of Liquified Natural Gas (LNG) and the systems required for generation and safe containment, it is likely a targeted cyber-attack on LNG control and safety systems will have a significant economic impact on energy supplies and prices. Moreover, if the interconnected operational technology (OT) devices within LNG systems are exploited to malfunction, the repair and recertification process will almost certainly be longer than for natural gas (NG) systems.

03 NATURAL GAS↗
DOE OSTI2026

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Data-Driven Probabilistic Anomaly Detection for Electricity Market under Cyber Attacks

Information and communication technologies have been widely used in smart grid for efficient operation. However, these technologies are vulnerable to malicious cyber attacks, which may lead to severe reliability and economic issues. Recently, a variety of data-driven anomaly detection approaches have been explored to detect potential cyber attacks in smart grids. In this paper, we researched on the electricity market data aiming to identify anomalies from the locational marginal prices (LMPs) and provide a new indicator for potential cyber attacks in power grids. Specifically, a novel data-driven probabilistic anomaly detection framework is proposed for electricity market, which consists of three major components: long short-term memory (LSTM) based deterministic electricity price forecasting, probabilistic electricity price forecasting and anomaly detection. This framework is tested on a model-based electricity market simulator under two types of cyber attacks, i.e., load redistribution attack (LRA) and price responsive attack (PRA). Numerical results on the simulated LMPs show that the proposed framework is capable of detecting data anomalies over these attacks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Anomaly Detection in Power System State Estimation: Review and New Directions

Foundational and state-of-the-art anomaly-detection methods through power system state estimation are reviewed. Traditional components for bad data detection, such as chi-square testing, residual-based methods, and hypothesis testing, are discussed to explain the motivations for recent anomaly-detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest-change detection and artificial intelligence are discussed, and directions for research are suggested with particular emphasis on considerations of the future smart grid.

42 ENGINEERING↗
DOE OSTI2014

Quantifying Cyber-Resilience Against Resource-Exhaustion Attacks

Resilience in the information sciences is notoriously difficult to define much less to measure. But in mechanical engi- neering, the resilience of a substance is mathematically defined as the area under the stress vs. strain curve. We took inspiration from mechanics in an attempt to define resilience precisely for information systems. We first examine the meaning of resilience in language and engineering terms and then translate these definitions to information sciences. Then we tested our definitions of resilience for a very simple problem in networked queuing systems. We discuss lessons learned and make recommendations for using this approach in future work.

cyber security↗