SEARCH · Engineering Papers
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.
This presentation by Erfan Ibrahim was prepared for NREL's 2017 Cybersecurity and Reslience Workshop on distributed energy resource (DER) best practices. The presentation provides an overview of NREL's Cyber-Physical Systems Security and Resilience R&D Center, the Center's approach to cybersecurity, and disruptive ideas for power grid security and resilience with DER.
In this paper, we will summarize a group of architectural principles that inform the development of secure control system architectures, followed by a methodology that allows designers to understand the attack surface of components and subsystems in a way that supports the integration of these surfaces into a single attack surface. We will then show how this methodology can be used to analyze the control system attack surface from a variety of threats, including knowledgeable insiders. We close the paper with an overview of how this approach can be folded into a more rigorous mathematical analysis of the system to define the system's security posture. (authors)
A modern power system is characterized by an increasing penetration of wind power, which results in large uncertainties in its states. These uncertainties must be quantified properly; otherwise, the system security may be threatened. Facing this challenge, here we propose a cost-effective, data-driven approach to assessing a power system's load margin probabilistically. Using actual wind data, a kernel density estimator is applied to infer the nonparametric wind speed distributions, which are further merged into the framework of a vine copula. The latter enables us to simulate complex multivariate and highly dependent model inputs with a variety of bivariate copulae that precisely represent the tail dependence in the correlated samples. Furthermore, to reduce the prohibitive computational time of traditional Monte-Carlo simulations that process a large amount of samples, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model through a Bayesian-learning framework. To accelerate the convergence rate of this Bayesian algorithm, a truncated polynomial chaos surrogate, which serves as a highly efficient, parametric Bayesian prior, is developed. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. Results of simulations that are performed on several test systems reveal the impressive performance of the proposed method in the probabilistic load-margin assessment.
This work examines a new approach to authentication, which is the most fundamental security primitive that underpins all cyber security protections. Current Internet authentication techniques require the protection of one or more secret keys along with the integrity protection of the algorithms/computations designed to prove possession of the secret without actually revealing it. Protecting a secret requires physical barriers or encryption with yet another secret key. The reason to strive for "Authentication without Secret Keys" is that protecting secrets (even small ones only kept in a small corner of a component or device) is much harder than protecting the integrity of information that is not secret. Promising methods are examined for authentication of components, data, programs, network transactions, and/or individuals. The successful development of authentication without secret keys will enable far more tractable system security engineering for high exposure, high consequence systems by eliminating the need for brittle protection mechanisms to protect secret keys (such as are now protected in smart cards, etc.). This paper is a re-release of SAND2009-7032 with new figures numerous edits.
Demand Response is capable of reducing the total amount of generation capacity investment required to ensure electricity system security. We utilise this fact to devise a novel methodology to estimate the contribution of a load-shifting demand response resource to system adequacy. We then simulate an electricity market using mixed complementarity models to determine how the participation of demand response in capacity markets impacts on market outcomes. Demand response primarily affects the equilibrium outcome through the energy market, however demand response also reduces both equilibrium prices and consumer costs through its capacity market contribution. The effect is particularly pronounced when there is a high level of variable renewable generation and initial undercapacity. In the absence of demand response, increased wind generation leads to higher capacity prices as generators seek to offset depressed energy prices. However, we find that demand response's participation in the capacity market can combat these increased capacity prices. These results suggest that demand response participation in capacity markets can mitigate some of the market challenges of renewable integration, particularly that of the 'missing money' problem.
Insider threats are perhaps the most serious challenges that nuclear and radiological security systems face. Insiders pose such a great threat due to their access, authority, and knowledge, granting them opportunities to bypass dedicated nuclear and radiological security elements. For example, in one of the latest major insider threat incidents to nuclear security, the Doel-4 nuclear powerplant in Belgium suffered a shutdown, the threat of nuclear materials diversion, and long-term loss of tens of millions of dollars. Seven years of investigation concluded that it was an inside job and attempted sabotage. In this regard, there is an immediate need for R&D and technology integration in the domain of modeling indoor Patterns-of-Life (PoL) and anomaly detection. This can be achieved by using datasets of facility users’ mobility and activity, which can support the design of algorithms for insider threat modeling and detection. However, due to classification, privacy, sensitivity, and safety protocols, such datasets from real physical nuclear reactor facilities are not only hard to share, but also not always feasible to deploy and collect. Aiming to find an alternate solution, our proposed demonstration work - MetaPoL, is the first-ever (for the application space) immersive VR (virtual reality) environment of a real-world secure facility and allows users to move-and-stay through the designed indoor physical layout and also encounter NPCs (non-player characters) that emulate other facility users. In the MetaPoL an interactive user performs realistic spatio-temporal movement, dwelling and activities using a Meta Quest Pro VR headset, and that generates high-frequency (in time) high-resolution (in space) indoor spatial-temporal datasets that are valuable for PoL modeling and anomaly detection research specifically for insider threat modeling and detection mission. Such generated realistic, rich in context, and mission specific datasets can boost AI/Machine Learning based research for modeling and detecting insider threats in nuclear security and nonproliferation.
The AFAS Database System was developed to provide the basic structure of a comprehensive database system for the Marshall Space Flight Center (MSFC) Structures and Dynamics Laboratory Aerophysics Division. The system is intended to handle all of the Aerophysics Division Test Facilities as well as data from other sources. The system was written for the DEC VAX family of computers in FORTRAN-77 and utilizes the VMS indexed file system and screen management routines. Various aspects of the system are covered, including a description of the user interface, lists of all code structure elements, descriptions of the file structures, a description of the security system operation, a detailed description of the data retrieval tasks, a description of the session log, and a description of the archival system.
Wave and tidal energy have the potential to provide benefits to power systems with high proportions of stochastic renewable generation. This is particularly applicable with wind and solar photovoltaics, as the offsetting of these renewable resources results in more reliable renewable generation. This study utilises ten metrics to quantify the temporal complementarity and supply-demand balancing requirements of the energy mix in Great Britain, to investigate the potential magnitude of these system benefits. Wave and tidal generation profiles are created using historical resource data and hydrodynamic models. The results show that the inclusion of wave and tidal generation creates a renewable energy mix which is more available under multiple conditions: throughout a year of operation; at times of peak demand; for multiple consecutive hourly time periods; and at times when wind and solar generation are not available. Three regional case studies also show that the inclusion of marine energy allows for improved regional supply-demand matching, reducing instances of energy shortage and excess and potentially relieving transmission congestion at particularly constrained locations within GB. Finally, the implications of these findings are discussed in terms of GB wholesale market operation, system balancing and system security.
Here, in this work we propose a novel data-driven, real-time power system voltage stability control method based on the physics-informed guided meta evolutionary strategy (ES). The main objective is to quickly provide an adaptive control strategy to secure system voltage stability. The problem is challenging due to the high-dimensional feature of the power system model and the fast-changing and uncertain nature of power system operation scenarios. To this end, a model-free and derivative-free guided ES method is applied. The method is further combined with a meta-learning strategy to make the learnt control policy automatically adapted to unseen operation conditions and fault scenarios, which is highly desired for real-time emergency control. Last but not least, physical knowledge is embedded in the above method through a trainable action mask technique to rule out unnecessary load shedding actions for better learning and control performance. Case studies on the IEEE 300-bus system and comparisons with other state-of-the-art benchmark methods verify the superiority of the proposed physics-informed guided meta ES method in realizing fast and adaptive power system voltage stability control.
The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.
Trouble in the classroom is an unpleasant fact of modern life. Space technology can't stop the trouble from occurring, but it can prevent it from spreading. In recognition of this, NASA and the Sacramento, Cal. Unified School District developed a personal security system based on space telemetry technology. The first application was for schools, but the simplicity and reliability of the system has made it more widely applicable. The heart of the system is an ultrasonic pen-size transmitter. It can be used by prison guards, teachers, or others such as the handicapped and the elderly.
Investigations within Security Operation Centers (SOCs) are tedious as they rely on manual efforts to query diverse data sources, overlay related logs, correlate the data into information, and then document results in a ticketing system. Security Orchestration, Automation, and Response (SOAR) tools are a relatively new technology that promise, with appropriate configuration, to collect, filter, and display needed diverse information; automate many of the common tasks that unnecessarily require SOC analysts’ time; facilitate SOC collaboration; and, in doing so, improve both efficiency and consistency of SOCs. There has been no prior research to test SOAR tools in practice; hence, understanding and evaluation of their effect is nascent and needed. Here, in this paper, we design and administer the first hands-on user study of SOAR tools, involving 24 participants and six commercial SOAR tools. Our contributions include the experimental design, itemizing six characteristics of SOAR tools, and a methodology for testing them. We describe configuration of a cyber range test environment, including network, user, and threat emulation; a full SOC tool suite; and creation of artifacts allowing multiple representative investigation scenarios to permit testing. We present the first research results on SOAR tools. Concisely, our findings are that: per-SOC SOAR configuration is extremely important; SOAR tools increase efficiency and reduce context switching, although with potentially decreased ticketing accuracy/completeness; user preference is slightly negatively correlated with their performance with the tool; internet dependence varies widely among SOAR tools; and balance of automation with assisting decision making is preferred by senior participants. We deliver a public user- and tool-anonymized and -obfuscated version of the data.
What is the meaning of risk as it applies to the design of advanced reactors in the disciplines of safety, security, and safeguards? How can we find common terminology for the concept of risk and how can we find interfaces between these disciplines? These are important questions that should be explored in order that they may be applied in an integrated manner for the most effective and efficient design approaches. Eliminating or minimizing risks is a key design driver that motivates and informs the development of nuclear reactors. For safety, risk is well understood and applied in Probabilistic Risk Assessments. For security, the risk-based concepts of vulnerability assessments and vital areas are all considered in designing security systems. For safeguards, the concept of risk is not formally defined, as it relates to the design and operation of nuclear reactors. International nuclear safeguards seek to reduce the risk of proliferation in the nuclear fuel cycle and as such the concept of risk does exist. Therefore, the current understanding of the “3S’ approach, which seeks to find the interfaces and conflicts between safety, security, and safeguards requires a thorough understanding of the role that the reduction of risk plays in all three disciplines. The intersection of risk for safety and security is now being developed as there is a strong correlation between reactor design and operations and their vulnerability to sabotage. The intersection of risk for security and safeguards has to date chiefly been focused on the nuclear material control and accounting systems, which are relied on by both the operator (State) and the IAEA. This paper explores the concept of risk in each of the three disciplines, how they interact, potential conflicts and interfaces , how these might be addressed and leveraged, and a notional framework for how this could be achieved.
This paper discusses security issues as they relate to the NASA Electronic Library System which is currently in use as the repository system for AdaNET System Version 3 (ASV3) being operated by MountainNET, Inc. NELS was originally designed to provide for public, development, and secure collections and objects. The secure feature for collections and objects was deferred in the initial system for implementation at a later date. The NELS system is now 9 months old and many lessons have been learned about the use and maintenance of library systems. MountainNET has 9 months of experience in operating the system and gathering feedback from the ASV3 user community. The user community has expressed an interest in seeing security features implemented in the current system. The time has come to take another look at the whole issue of security for the NELS system. Two requirements involving security have been put forth by MountainNET for the ASV3 system. The first is to incorporate at the collection level a security scheme to allow restricted access to collections. This should be invisible to end users and be controlled by librarians. The second is to allow inclusion of applications which can be executed only by a controlled group of users; for example, an application which can be executed by librarians only. The requirements provide a broad framework in which to work. These requirements raise more questions than answers. To explore the impact of these requirements a top down approach will be used.
Aviation terrorism is powerful and symbolic, and will likely remain a staple target for terrorists aiming to inflict chaos and cause mass casualties similar to the 9/11 attacks on the U.S. The majority of international and domestic aviation terrorist attacks involves outsiders, or people who do not have direct access to or affiliation with a target through employment. However, several significant attacks and plots against the industry involved malicious employees motivated by suicide or devotion to a terrorist organization. Malicious insiders’ access and knowledge of aviation security, systems, networks, and infrastructure is valuable to terrorists, providing a different pathway for attacking the industry through the insider threat. Indicators and warnings of insider threats in these cases exist, providing insight into how security agencies, such as the Transportation Security Administration, can better predict and identify insider involvement. Understanding previous aviation insider threat events will likely aid in stimulating proactive security measures, rather than reactive responses. However, similar to traditional airport security measures, there are social, political, and economic challenges in protecting against the insider threat, including privacy concerns and cost-benefit analysis.
Domestic extremism (DE) has been a growing concern in the U.S. in recent months, as illustrated in multiple bulletins from the Department of Homeland Security (DHS) warning law enforcement partners of the heightened threat. As concerns about these actors grows, it is important that facilities in the U.S. and internationally that protect critical assets, such as sensitive information, hazardous materials, or critical infrastructure, have effective methods in place to secure those assets. DE has challenged security systems through the danger of insider attack and violence, creating a new threat to be countered. In this effort, therefore, we used a literature review and focus group discussions with experts in critical asset security and extremism to understand the nature of the domestic extremist threat, to identify best practices in securing assets, recognize potential gaps in security measures to be corrected, and recommend actions for the Office of Radiological Security (ORS) to address DE with its partners.
This document provides a methodology for the performance-based assessment of security systems designed for the protection of nuclear and radiological materials and the processes that produce and/or involve them. It is intended for use with both relatively simple installations and with highly regulated complex sites with demanding security requirements.
Protocols play an essential role in Advance Reactor systems. A diverse set of protocols are available to these reactors. Advanced Reactors benefit from technologies that can minimize their resource utilization and costs. Evaluation frameworks are often used when assessing protocols and processes related to cryptographic security systems. The following report discusses the various characteristics associated with these protocol evaluation frameworks, and derives a novel evaluative framework.