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Cyber supply chain risk management (C-SCRM) programs must consider operations that depend on the lifecycles of digital components such as hardware, firmware, software, and services. We integrate academic literature, historical incidents, and existing standards to identify obstacles faced by C-SCRM programs.
This presentation provides and overview of the Federated Architecture for Secure and Transactive Distributed Energy Resource Management Solutions (FAST-DERMS) project and progress to date as of December 2024.
Brookhaven National Laboratory (BNL) is managed on behalf of the Department of Energy (DOE) by Brookhaven Science Associates (BSA), a partnership between the Research Foundation for the State University of New York on behalf of Stony Brook University and Battelle. For over 70 years, the Laboratory has played a lead role in the DOE Science and Technology mission and continues to contribute to the DOE’s missions in energy resources, environmental quality, and national security. BNL manages its world-class scientific research with particular sensitivity to environmental issues and community concerns. The Laboratory’s Environmental, Safety, Security, and Health (ESSH) Policy reflects the commitment of BNL’s management to fully integrate environmental stewardship into all facets of its mission and operations. BNL prepares an annual Site Environmental Report (SER) in accordance with DOE Order 231.1B, Environment, Safety, and Health Reporting. The report is written to inform the public, regulators, employees, and other stakeholders of the Laboratory’s environmental performance during the calendar year in review. Volume I of the SER summarizes environmental data; environmental management performance; compliance with applicable DOE, federal, state, and local regulations; and performance in restoration and surveillance monitoring programs. BNL has prepared annual SERs since 1971 and has documented nearly all its environmental history since the Laboratory’s inception in 1947. Volume II of the SER, the Groundwater Status Report, is also prepared annually to report on the status of groundwater protection and restoration efforts. Volume II includes detailed technical summaries of groundwater data and treatment system operations and is intended for regulators and other technically oriented stakeholders. A summary of the information contained in Volume II is included in Chapter 7, Groundwater Protection, of this volume.
Brookhaven National Laboratory (BNL) is managed on behalf of the Department of Energy (DOE) by Brookhaven Science Associates (BSA), a partnership between Stony Brook University and Battelle, and six core universities: Columbia, Cornell, Harvard, Massachusetts Institute of Technology, Princeton, and Yale. For over 75 years, the Laboratory has played a lead role in the DOE Science and Technology mission and continues to contribute to the DOE’s missions in energy resources, environmental quality, and national security. BNL manages its world-class scientific research operations with sensitivity to environmental issues and community concerns. The Laboratory’s Environmental, Safety, Security, and Health (ESSH) Policy reflects the commitment of BNL’s management to fully integrate environmental stewardship into all facets of its mission and operations. BNL prepares an annual Site Environmental Report (SER) in accordance with DOE Order 231.1B, Environment, Safety, and Health Reporting. The report is written to inform the public, regulators, employees, and other stakeholders of the Laboratory’s environmental performance during the calendar year in review. Volume I of the SER summarizes environmental data; environmental management performance; compliance with applicable DOE, federal, state, and local regulations; and performance in restoration and surveillance monitoring programs. BNL has prepared annual SERs since 1971 and has documented nearly all its environmental history since the Laboratory’s inception in 1947. Volume II of the SER, the Groundwater Status Report, is also prepared annually to report on the status of groundwater protection and restoration efforts. Volume II includes detailed technical summaries of groundwater data and treatment system operations and is intended for regulators and other technically oriented stakeholders. A summary of the information contained in Volume II is included in Chapter 7, Groundwater Protection, of this volume.
Brookhaven National Laboratory (BNL) is managed on behalf of the Department of Energy (DOE) by Brookhaven Science Associates (BSA), a partnership between Stony Brook University and Battelle, and six core universities: Columbia, Cornell, Harvard, Massachusetts Institute of Technology, Princeton, and Yale. For over 75 years, the Laboratory has played a lead role in the DOE Science and Technology mission and continues to contribute to the DOE’s missions in energy resources, environmental quality, and national security. BNL manages its world-class scientific research operations with sensitivity to environmental issues and community concerns. The Laboratory’s Environmental, Safety, Security, and Health (ESSH) Policy reflects the commitment of BNL’s management to fully integrate environmental stewardship into all facets of its mission and operations. BNL prepares an annual Site Environmental Report (SER) in accordance with DOE Order 231.1B, Environment, Safety, and Health Reporting. The report is written to inform the public, regulators, employees, and other stakeholders of the Laboratory’s environmental performance during the calendar year in review. Volume I of the SER summarizes environmental data; environmental management performance; compliance with applicable DOE, federal, state, and local regulations; and performance in restoration and surveillance monitoring programs. BNL has prepared annual SERs since 1971 and has documented nearly all its environmental history since the Laboratory’s inception in 1947. Volume II of the SER, the Groundwater Status Report, is also prepared annually to report on the status of groundwater protection and restoration efforts. Volume II includes detailed technical summaries of groundwater data and treatment system operations and is intended for regulators and other technically oriented stakeholders. A summary of the information contained in Volume II is included in Chapter 7, Groundwater Protection, of this volume.
Brookhaven National Laboratory (BNL) is managed on behalf of the Department of Energy (DOE) by Brookhaven Science Associates (BSA), a partnership between Stony Brook University and Battelle, and six core universities: Columbia, Cornell, Harvard, Massachusetts Institute of Technology, Princeton, and Yale. For over 75 years, the Laboratory has played a lead role in the DOE Science and Technology mission and continues to contribute to the DOE’s missions in energy resources, environmental quality, and national security. BNL manages its world-class scientific research operations with sensitivity to environmental issues and community concerns. The Laboratory’s Environmental, Safety, Security, and Health (ESSH) Policy reflects the commitment of BNL’s management to fully integrate environmental stewardship into all facets of its mission and operations.
Risk Informed Timelines (RIT) is a more modern and holistic method for understanding how access delay timelines are developed and utilized in physical security system evaluations. The software utilizes Bayesian methods to combine subject matter expert judgement and small performance test datasets in a consistent and defensible way. It allows a more holistic view of delay performance that provides distributions of task times and task success probabilities to account for tasks that, if failed, would result in failure of the attack. The RIT methodology is meant to enable security risk managers to begin the transition into more comprehensive delay timeline analysis. 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. SAND2021-8294 O
The adoption of clean energy technologies, including solar photovoltaics, continues to introduce non-traditional stakeholders to the operations and planning of the electric system. Stakeholders such as manufacturers, vendors, owners, aggregators, and others are enabling the adoption, integration, and optimum operations of solar technologies at accelerated rates. Inverters form the foundation of many digitally controlled energy sources for clean energy technologies, including Solar, Battery Energy Storage Systems, Hybrid Systems, and Hydrogen Fuel Cells. Their supply chain is complex, a series of microchips, electronic switches and other components making up its primary functions. The complexity of this space and the growing digitization associated with these components can create supply chain cyber risks. One measure to mitigate cybersecurity attacks is proper digital supply chain security. The U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO), in partnership with the Cybersecurity, Energy, Security, and Emergency Response (CESER) office, is hosting a workshop to bring together solar vendors and services providers to discuss digital supply chain security for solar systems and challenges and opportunities in the transitioning to a fully domestic supply chain for solar energy in the U.S. This workshop will support the Securing Solar for the Grid (S2G) and Energy Cyber Sense program activities. During the workshop, industry experts and researchers from DOE National Laboratories will discuss the current solar supply chain landscape and the transition to domestic manufacturing of solar components in the U.S. Tools and techniques to better manage and secure the digital supply chain of solar devices and systems will be discussed.
In 1995, NASA s Jet Propulsion Laboratory (JPL) contracted Redmond, Washington-based Lucidoc Corporation, to design a technology infrastructure to automate the intersection between policy management and operations management with advanced software that automates document workflow, document status, and uniformity of document layout. JPL had very specific parameters for the software. It expected to store and catalog over 8,000 technical and procedural documents integrated with hundreds of processes. The project ended in 2000, but NASA still uses the resulting highly secure document management system, and Lucidoc has managed to help other organizations, large and small, with integrating document flow and operations management to ensure a compliance-ready culture.
This paper recommends an approach to inadvertent human intrusion (IHI) at the Area 3 and Area 5 Radioactive Waste Management Sites (RWMSs) on the Nevada National Security Site (NNSS). IHI analysis uses the consequences of an individual inadvertently contacting buried waste to set waste concentration limits for near-surface disposal of low-level radioactive waste (LLW). Regulatory agencies are increasingly applying risk-informed decision-making to LLW waste management (NRC 2006). Risk-informed decision-making combines scientific risk assessment with stakeholder values and perceptions to determine a level of acceptable risk. Risk considers not only the consequences of an event, but also its probability of occurring.
The extent and duration of French dismantling operations require to anticipate at the earliest possible stage the management of the large volumes of radioactive waste that will be generated, and to engage collectively in sustainable innovation processes. To anticipate all constraints and requirements related to radioactive waste management and secure the long-term compatibility with waste disposals, the French State has decided to give to Andra, within the 'Investissements d'Avenir' program, an endowment to support innovation at all steps of radioactive waste life cycle with a special emphasis on the dismantling process. Currently, some 30 R and D projects dealing with the optimization of dismantling radioactive waste management are underway. They aim at providing strong innovations in terms of characterization, processing, conditioning and disposal of post dismantling radioactive waste. A specific focus will be made on those dealing with radioactive waste and radionuclides recycling. (authors)
Oak Ridge National Laboratory (ORNL) is leveraging its established capabilities and subject matter expertise in data curation, governance, management, national security, and risk assessment and mitigation to support the US Department of Energy (DOE) Grid Modernization Initiative. Using standards modeled by the National Institute of Standards and Technology (NIST), the Data Curation Network (DCN), the Oak Ridge Leadership Computing Facility (OLCF), and other leading organizations in the fields of energy research, high-performance computing, and national and homeland security, ORNL seeks to provide a federated approach to research data discovery, use, and interoperability.
Smart grid technologies have rapidly become one of the largest and most comprehensive sources of data for the modern utility. For the most part, data streams are seen as an essential tool that enable utilities to carry their day-to-day business operations, but they also create the need for efficient and secure data management strategies. In the context of the smart grid, ensuring data privacy is becoming an increasing concern due to a combination of factors that range from shifts in operational paradigms and rapid technology evolution to changes in legislation. Furthermore, researchers have highlighted the risks associated with improperly protected energy records. For example, energy consumption data from homes could be used to infer the behaviors and habits of home occupants through activity recognition or user profiling (Fan, 2017), which may lead to unfair service pricing, targeted advertising, or other personal security violations. Similarly, Electric Vehicles’ (EVs) charging metadata could be used to reveal private information about the owner such as their payment methods, preferred charging stations, and other locational and timing information that could be used to reconstruct the vehicle owner’s behaviors. The privacy of user data, even when used for statistical analysis or machine learning training processes, also needs to be carefully considered, as an individual’s private traits may still be vulnerable if their inclusion/exclusion greatly impacts the result or could be linked to a public dataset through cross-reference. The breach of user privacy also has severe impacts for organizations that store, transmit, or work on the data in the form of diminishing the public’s trust in them while potentially incurring legal consequences (e.g., fines and suspensions under the European Union General Data Protection Regulation, Health Insurance Portability and Accountability Act, etc.). Because of these risks, several privacy-preserving mechanisms are available to help organizations comply with privacy legislations and prevent the unauthorized and malicious use of user data. In light of these concerns, this report focuses on performing a computational review of privacy-preserving mechanisms that have received a significant amount of interest in literature. It specifically focuses on 1) homomorphic encryption, 2) zero-knowledge proofs, 3) differential privacy, and 4) federated learning. It is worth noting that although many of the methods presented in this document rely on cryptographic primitives, their intent is not to provide perfect secrecy, but rather to enable users to maintain privacy, and thus they shall not be compared or equated to other constructs that are aimed to address cybersecurity constructs.
We present R2U2 (Realizable, Responsive, Unobtrusive Unit), a hardware- supported tool and framework for the continuous monitoring of safetycritical and embedded cyber-physical systems.With the widespread advent of autonomous systems such as Unmanned Aerial Systems (UAS), satellites, rovers, and cars, real-time, on-board decision making requires unobtrusive monitoring of properties for safety, performance, security, and system health. R2U2 models combine past-time and future-time Metric Temporal Logic, “mission time” Linear Temporal Logic, probabilistic reasoning with Bayesian Networks, and modelbased prognostics. The R2U2 monitoring engine can be instantiated as a hardware solution, running on an FPGA, or as a software component. The FPGA realization enables R2U2 to monitor complex cyber-physical systems without any overhead or instrumentation of the flight software. In this tool exhibition report, we present R2U2 and demonstrate applications on system runtime monitoring, diagnostics, software health management, and security monitoring for a UAS. Our tool demonstration uses a hardware-based processor-in-the-loop “iron-bird” configuration.
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In early 2015 the United States Department of Energy conceived of a consortium of collaborative bodies based on shared expertise, data, and resources that could be targeted towards the more difficult problems in energy materials research. The concept of virtual laboratories had been envisioned and discussed earlier in the decade in response to the advent of the Materials Genome Initiative and similar scientific thrusts. To be effective, any virtual laboratory needed a robust method for data management, communication, security, data sharing, dissemination, and demonstration to work efficiently and effectively for groups of remote researchers. With the accessibility of new, easily deployed cloud technology and software frameworks, such individual elements could be integrated, and the required collaboration architecture is now possible. The developers have leveraged open-source software frameworks, customized them, and merged them into a platform to enable collaborative energy materials science, regardless of the geographic dispersal of the people and resources. After five years in operations, the systems are demonstratively an effective platform for enabling research within the Energy Material Networks (EMN). This paper will show the design and development of a secured scientific data sharing platform, the ability to customize the system to support diverse workflows, and examples of the enabled research and results connected with some of the Energy Material Networks.
This study investigates the crucial role energy leaders play in driving strategic energy management (SEM) and accelerating cost savings within a manufacturing organization and consequently, the industrial sector. Whereas energy efficiency can be seen as an innovative business practice with irrefutable cost benefits, its effective implementation requires strategic leadership and a structured approach. This research analyzes data collected from 120 participants representing 71 companies attending the Energy Bootcamp events organized by the U.S. Department of Energy’s (DOE) Better Plants program. The collected data focused on the state of SEM implementation, the presence and responsibilities of energy leaders, and the formation and function of energy teams. The findings reveal a significant gap between the perceived importance of SEM and its actual adoption, highlighting the need for strong leadership to drive behavioral changes by championing energy efficiency initiatives. Results indicate that effective energy leaders possess a diverse skill set, including the ability to secure top management buy-in, foster a culture of energy consciousness, and collaborate across departments. This study emphasizes the importance of empowering energy leaders with clearly defined roles and responsibilities as well as the authority to build and lead cross-functional energy teams. Furthermore, integrating energy management into existing organizational structures and leveraging readily available resources are identified as key factors for successful implementation. This research underscores how dedicated leadership and effective SEM practices help achieve industrial energy efficiency goals, providing practical insights for organizations seeking to improve performance and contribute to a resilient future.
Low-resolution converters reduce the power consumption of wireless devices and help enable large energy-efficient arrays. In this letter, we evaluate the secrecy rate for the extreme case of 1-bit single-user multiple-input multiple-output systems following two signaling approaches. In the first method, the system uses discrete signaling. In the second, the system combines Gaussian signaling with artificial noise alongside power allocation. In the first case, we give a closed-form expression of the secrecy rate and propose an algorithm that provides near-optimal results for low signal-to-noise ratios (SNRs). Further, in the second scenario, depending on the channel state information, we suggest different precoding plans and derive a secrecy rate lower bound. At low SNR, power is allocated to the main signal. Only at high SNR is optimal power allocation effective. Moreover, knowing the eavesdropper channel greatly improves the system’s secrecy.