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At least 397 records · Page 22

GridSTIX

SF-25-112 Grid-STIX is a comprehensive extension of the STIX (Structured Threat Information Expression) 2.1 ontology specifically designed for electrical grid cybersecurity applications. This ontology provides a standardized, machine-readable framework for modeling grid assets, operational technology devices, threats, vulnerabilities, supply chain risks, and security relationships in electrical power systems. ## Key Features - **Comprehensive Grid Coverage**: Physical assets, OT devices, grid components, sensors, and energy storage systems - **Zero Trust Architecture**: Policy decision points, enforcement points, trust brokers, and continuous monitoring - **AMI Infrastructure**: Advanced metering networks, head-end systems, mesh gateways, and MDM systems - **Advanced Security Modeling**: Attack patterns, vulnerabilities, mitigations, and supply chain risks - **Critical Grid Relationships**: Power flow, protection, control, and synchronization relationships - **Supply Chain Security**: Supplier modeling, country of origin tracking, and risk assessment - **Protocol Support**: DNP3, Modbus, IEC 61850, IEC 60870-5-104, OPC-UA, and IEEE standards - **Python Code Generation**: Automated STIX-compliant Python class generation from ontologies - **Interactive Visualization**: Enhanced HTML network graphs with grid-specific categorization - **STIX 2.1 Compliance**: Full compatibility with STIX threat intelligence ecosystem

Blakely, Benjamin [Argonne National Laboratory (AN↗

Connecting Nuclear Security to International Frameworks on Gender and Security

The international community is slowly beginning to recognize the intersections between the law and policy on international security – particularly arms control, non-proliferation, and disarmament – and the body of human rights law that addresses gender equality. Notably absent from this discussion is the field of nuclear security. Despite its historical underpinnings as an inherently domestic activity, nuclear security is thoroughly grounded in international treaty law. Yet, nuclear security is often overlooked in the international security context and has not been well situated with international instruments that address gender equality. We argue that gender equality in nuclear security should be understood as an important component of broader efforts to achieve equal opportunities for women in work and critical to ensuring women are included in conflict prevention efforts. Linking nuclear security to broader international efforts to increase gender equality in security and conflict prevention will provide a clearer structure and framework for gender equality initiatives in the nuclear security field. This is critically important given that estimates indicate that women comprise only twenty percent of the nuclear security workforce. Moreover, situating nuclear security in a broader international legal framework will simultaneously help states meet their gender equality commitments emanating from other instruments. This paper will first analyze the relationship between nuclear security and broader international security efforts, in particular arms control treaties and non-proliferation regimes. It will then survey the relevant international and regional frameworks for gender equality, particularly those that have applicability in the security context. This paper will next explore the relationship between nuclear security and these frameworks on gender. We find that some instruments provide support for gender equality initiatives in nuclear security because of their mandate to states to provide structural gender equality, and others are particularly relevant where they call for women's participation in conflict prevention. This paper concludes with recommendations to states that are concerned about the underrepresentation of women in nuclear security.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Tractable Learning in Underexcited Power Grids

Estimating the structure of physical flow networks, such as power grids, is critical to secure delivery of energy. This article discusses statistical structure estimation in power grids in the “underexcited” regime, where a subset of internal nodes has zero injection fluctuations. Prior estimation algorithms based on nodal voltages fail for such grids as the voltage covariance matrix is not invertible. Here, we propose a novel topology learning algorithm for learning underexcited general networks. Our algorithm uses physics-informed conservation laws to first identify the zero-injection buses and their neighbors, and then estimates the remaining edges in the grid. We prove the asymptotic correctness of our algorithm for grids with nonadjacent internal zero-injection nodes. More important, we theoretically analyze our algorithm’s efficacy under noisy measurements, and determine bounds on maximum noise under which asymptotically correct recovery is guaranteed. Our approach is validated through simulations with voltage samples generated on test distribution grids with real injection data and nonlinear power flow models.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Identifying neutron sources using recoil and time-of-flight spectroscopy

Identification of neutron sources is central to nuclear physics and its applications, from planetary science to nuclear security, yet direct source discrimination from measured neutron spectra remains fundamentally elusive. Here, we introduce a Bayesian protocol that directly infers source ensembles from measured neutron spectra by combining full-spectrum template matching with probabilistic evidence evaluation. Applying this protocol to recoil and time-of-flight spectroscopy, we recover single- and two-source configurations with strong statistical significance (beyond 4⁢𝜎) at event counts as low as ∼10 3 . These results demonstrate that neutron spectral signatures can be leveraged for robust source identification, opening a new observational window for both fundamental research and operationally driven applications.

neutron physics↗

Building Blocks for Secure and Prosperous Defense Critical Supply Chains: A Case Study from Microelectronics

Securing defense-critical supply chains, built on resilient and sustainable microelectronics fabrication and deployment, is a national imperative and one that requires an investment in basic and applied research, development, and deployment into industries in (and out of) the Defense Industrial Base (DIB). Critical next steps include the development of pilots for revolutionary concepts in a new formal verification model and the Cyber-Physical Passport (CPP) concept for chain of custody. Critical infrastructure leadership should take action with a sense of urgency. Working in conjunction with the Under Secretary for Acquisition and Sustainment, the Assistant Secretary of Defense for Research and Engineering should establish pilot programs with the Defense Advanced Research Projects Agency and the service labs to build and test both concepts in legacy and new system development.. Additionally, the Pentagon may be aware that an existing Manufacturing Innovation Institute is piloting, with the semiconductor industry, cyber innovations to provide verifiable security properties and guarantees of physical functions to build a more cyber secure, resilient and efficient micro-electronics supply chain.

99 GENERAL AND MISCELLANEOUS↗

2022 Agnew and Metropolis Postdoc Fellow Showcase Event Book

Agnew National Security Postdoc Fellows pursue cutting-edge experimental, theoretical, computational science, and engineering research aligned with the national security mission. Metropolis Postdoc Fellows pursue cutting-edge research in the areas of computational and computer science, physics, and engineering. Computer simulation capabilities are developed in support of the stockpile stewardship program together with broader national nuclear security needs. Fellows have access to some of the most powerful supercomputers in the world to perform pioneering research.

42 ENGINEERING↗

Deliberate Motion Analytics Fused Radar and Video Test Results Deployed Beyond the Perimeter Fence in a High Noise Environment

Security systems that protect the nation’s critical facilities must be capable of detecting physical intrusions in all weather conditions. Intrusion detection sensors in a perimeter with a high nuisance alarm rate (NAR) significantly undermine detection performance and degrade security system effectiveness. This research demonstrated a fused sensor system that can differentiate foliage and weather-induced nuisance alarms from those caused by intruders, providing reliable detection within a two-fence perimeter or beyond the fence. A key element of this work is the creation and application of a “deliberate motion algorithm” that fuses alarm data from radar and video analytics to create video motion detection fused radar system. The two-layer architecture of the algorithm uses machine learning, multi-hypothesis tracking, and Dynamic Bayes Nets to differentiate intruder alarms from weather induced alarms.

47 OTHER INSTRUMENTATION↗

Incremental Threshold Scheme Enabled IoT Group Key Management

Cyber landscape evolves rapidly. Internet of Things (IoT) and Edge Computing (EC) have rapidly become an integral part of the modern computing infrastructure. It is expected that there will be more than 50 billion active and connected IoT devices by 2025 [1]. Pervasive IoT/EC creates unprecedented opportunities bridging the gap between previously segregated cyber and physical spaces. However, this progress also brings along new security challenges. IoT devices typically have limited computation, communication, and storage resources. This leads to security architecture designs such as using symmetric keys for group communication. While secure and efficient in stable network settings, symmetric key solutions are ill-adapted for IoT's highly dynamic device mobility behavior and frequent group membership turnover. Whenever IoT members leave a group, the known symmetric keys cannot be made forgotten, posing a serious vulnerability. This leads to frequent re-groupings that require expensive re-authentication, key regeneration, and key redistribution in order to maintain IoT/EC security. We present a novel symmetric key management framework that integrate an Incremental Threshold Scheme (ITS) cryptographical function into communication protocol's key rotation mechanism to allow for secure and efficient symmetric key communication group member node revocation. This ITS-enabled key management framework alleviates the need of frequent and expensive re-grouping and re-keying needed by today's large and dynamic IoT/EC operations. We further applied this ITS-enabled key management framework to a distributed IoT/EC-integrated publish and subscribe framework for applicability validation.

Li, Mingyan↗

Fabrication of Low-Cost Large-Volume Ceramic A 2 HfX 6 (A= Cs or Tl, X = Cl, Br, or I) Scintillators for Gamma Ray Detection (SBIR Phase I Final Technical Report)

Scintillator crystals play an important role in the radiation detection field. Widespread use of scintillators as gamma-ray detectors is largely generated by their extensive availability and tunable properties, such high light output, high stopping power (Z eff ), fast decay time, and good proportionality. Additionally, the cost for manufacturing a scintillation detector like NaI:Tl is usually considerably lower than the cost for manufacturing a semiconductor detector like CdZnTe. Because there is no such thing as an ideal scintillation material, an application requiring certain detection characteristics may incorporate a scintillator tailored to its specific properties. The vast variety of applications and requirements necessitates more research into new scintillation materials and/or better methods of producing existing materials.The goal of this project was to grow low cost and environmentally stable inorganic transparent ceramic scintillators with excellent gamma ray resolution, excellent energy proportionality, excellent detection efficiency due to high density (>5 g/cm 3 ) and very high Z eff (55-80), and good light yields (>40,000 ph/MeV). In Phase I Xtallized Intelligence, Inc. (XI, Inc) developed a novel ceramic fabrication technique to produce low cost and environmentally stable highly efficient inorganic transparent ceramic scintillators of various dimensions. XI, Inc., collaborating with Fisk University (Fisk), investigated the scintillation properties of these new ceramic scintillators and compared them to in their single crystal counterparts. The results of this Phase I project show that successful production of high-quality inorganic halide ceramic scintillators Cs 2 HfCl 6 (CHC) and Tl 2 HfCl 6 (THC). Both ceramic CHC and THC scintillators have achieved good performance close to the performance of their single crystal counterparts. Fabricating these inorganic ceramic scintillators mitigate many issues encountered during conventional bulk crystal growth by melt methods. Additional benefits of the ceramic fabrication technique include high production yield, low production cost, fast production time, and no material waste Inorganic transparent ceramic scintillators produced in this project will enhance cost effectiveness at the instrument level based on low projected cost of the proposed compounds, as much smaller crystal sizes would be required to achieve similar efficiency as current radioisotope identification devices (RIID’s) used in homeland security applications as well as spectrometers in high energy physics applications.

36 MATERIALS SCIENCE↗

International Nuclear Security Nuclear Material Accounting and Control Instructions for Use and Supplementary Documentation

This document describes the use of exercise kits (called NMAC Kits) developed to support training and technical engagements sponsored by DOE/NNSA Office of Nuclear Security (INS) in the use of nuclear material accounting and control (NMAC) methodologies in support of nuclear security. INS has previously used other similar kits that were originally designed to support NMAC training for international safeguards purposes. These new kits are specifically designed to be used in nuclear security training and emphasis the role NMAC plays in nuclear security as well as security against the insider threat. Planning for the development of these kits is described in LA-UR-24-30063, FY24 NMAC Kits Upgrade and served as the initial framework for the development of the new NMAC Kit material in FY25.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.

quantitative risk assessment, geologic carbon stor↗

Quantum Key Distribution Applicability to Smart Grid Cybersecurity Systems

To meet the increasing demand for electricity and to have a more reliable and resilient electric grid against conventional and extreme events, grid modernization is more crucial now than ever before. This will require the development and deployment of devices that provide advanced communication capabilities. The overall efficiency, reliability, and resilience of the smart grid will be inextricably linked to the exchange of information between these devices. Unfortunately, the increased information flow will increase the potential attack surface and introduce new vulnerabilities. While a smarter grid will depend critically on information flow, these benefits will be accrued only if that information can be protected. Nowadays, information is secured in smart grids primarily through cryptography. However, with the increasing number of sophisticated attacks as well as the increasing computational power, the security of the “classical” cryptographic algorithms is threatened. Quantum information science offers solutions to this problem, specifically quantum key distribution (QKD), which provides a means for the generation and secure distribution of symmetric cryptographic keys. The security of QKD stems ultimately from the very nature of quantum physics. In this paper, we investigate the applicability of QKD to the various smart grid sectors and specific use cases. We have identified 18 smart grid use cases of interest for QKD suitability together with 7 QKD factors used for the assessment of the various use cases. For each use case, the impact to security of the loss of confidentiality, integrity, and/or availability is specified. In addition, the suitability of QKD is assessed for each use case with respect to multiple factors.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Cyber-Attack Detection for Photovoltaic Farms Based on Power-Electronics-Enabled Harmonic State Space Modeling

Here in this paper, a physics-data-based detection method is proposed to detect a variety of cyber-attacks in Photovoltaic (PV) farms using the power electronics-enabled harmonic state space (HSS) models, which, to our knowledge, is original. At the device level, HSS-based detection is developed to monitor harmonic vectors of individual PV converter with minimum sensor measurements, thus improving accuracy and robustness compared to Kalman Filter-based detection. At the system level that involves multiple PV converters, a clustering approach is developed to investigate attack propagation and accurately locate attack sources within a PV farm. The proposed approach is one of the first attempts to address PV security through interaction between the device and system, maximizing the accuracy and robustness at different levels. To verify the feasibility, a comprehensive attacks model is built, including single attack, coordinated attacks, and replay attacks. Besides, the impacts of irradiance changes are taken into consideration in the test scenarios. With the real-time data acquisition and hardware-in-the-loop testbed, comprehensive test results are provided to verify the feasibility of the proposed detection methodology.

42 ENGINEERING↗

WISP: Watching grid Infrastructure Stealthily through Proxies (Final Technical Report)

The complex interdependencies of cyber systems (sensors and communications), physical grids and associated electricity market operations make protecting electric power grids a significant challenge. The energy sector is constantly under new, targeted, advanced and dangerous cyber-attacks that have the potential to result in the loss of human life. These threats are further exacerbated by our need to modernize the grid. One focus of cyber security research in smart grids is the securing of the SCADA system through advanced intrusion detection systems (IDS) and bad data detection algorithms in state estimation. These methods either require full knowledge of the system topology and parameters or fail to understand the physical behaviors under attack. WISP (Watching grid Infrastructure Stealthily through Proxies) is designed to provide additional protection to the power grid using only publicly available data. In particular, WISP exploits the spatio-temporal nature of the real time locational marginal prices (LMPs), in conjunction with other information such as bids, weather, outages and load data to analyze anomalous power pricing behaviors and then correlate those observations to localize regions of interest and identify potential cyber events. WISP is non-intrusive as the tool is deployed as a service in the Cloud or on premise and provides reliable information to system operators for enhanced situational awareness, without impeding energy delivery functions. The WISP technology comprises three modules: the data-driven anomaly detection core, the vulnerability and risk analysis and the root cause analysis. The data-driven anomaly detection core performs the tasks of feature selection, anomaly detection and attack region localization. The vulnerability and risk analysis module provides system level information of the vulnerable variables and times, assisting the operators in selecting monitoring and protection nodes. The root cause analysis module takes the detection results and identifies potential operational conditions that contribute to the detected anomalies. In Phase I, we have demonstrated the feasibility and effectiveness of WISP. We developed a realistic electricity market simulator capable of generating normal and attack market data under various operational conditions. We developed a series of cyber-attack detection and analysis algorithms and evaluated them under multiple data sources. Finally, we integrated all modules into an end-to-end software, providing functions for data management, data analytics and visualization. Specifically, we have achieved: (i) real-time data acceptance from external utility interfaces with >99% acceptance rate; (ii) high performance anomaly detection algorithms with >98% detection accuracy and <0.1% false alarm rate; and (iii) ultra-low computing delay <50 milliseconds. Additionally, our team developed algorithms to identify the vulnerable variables in electricity market operations and root cause analysis functions to identify major contributors to the price spikes. These ancillary modules are necessary when deploying WISP in real world industry environment. In Phase II, we have demonstrated the effectiveness of WISP software on realistic largescale power systems. We performed red team testing for the Phase I WISP software and identified software vulnerabilities and implemented corresponding mitigation solutions. We adapted the electricity market simulator for the Texas synthetic 2000-bus system and generated datasets for the false data injection attacks. We created database and visualization interfaces for the Texas system and the ISO New England system. We performed software optimization in terms of operation efficiency, computing speed and detection accuracy. Finally, we tested the software on the Texas system and the ISO New England system and evaluated the detection performance. Overall, we achieved above 89% detection rate, below 3% false alarm rate and below 37 seconds of end-to-end detection delay.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena

Abstract Climate variability and weather phenomena can cause extremes and pose significant risk to society and ecosystems, making continued advances in our physical understanding of such events of utmost importance for regional and global security. Advances in machine learning (ML) have been leveraged for applications in climate variability and weather, empowering scientists to approach questions using big data in new ways. Growing interest across the scientific community in these areas has motivated coordination between the physical and computer science disciplines to further advance the state of the science and tackle pressing challenges. During a recently held workshop that had participants across academia, private industry, and research laboratories, it became clear that a comprehensive review of recent and emerging ML applications for climate variability and weather phenomena that can cause extremes was needed. This article aims to fulfill this need by discussing recent advances, challenges, and research priorities in the following topics: sources of predictability for modes of climate variability, feature detection, extreme weather and climate prediction and precursors, observation–model integration, downscaling, and bias correction. This article provides a review for domain scientists seeking to incorporate ML into their research. It also provides a review for those with some ML experience seeking to broaden their knowledge of ML applications for climate variability and weather.

54 ENVIRONMENTAL SCIENCES↗

Security Assessment of an LBP16-Protocol-Based Computer Numerical Control Machine

Subtractive manufacturing systems, specifically, computer numerical control machines, have revolutionized the manufacturing industry. Computer numerical control machining is the preferred method for producing finished parts due to its efficiency, speed and suitability for high-volume production. Securing computer numerical control machines is a priority. Compromises or disruptions of these machines can result in significant downtime, loss of productivity and financial loss. This study examines the vulnerabilities and risks associated with computer numerical control machines, in particular, systems utilizing the LBP16 protocol for controller-machine communications. The study reveals that an adversary can execute cyber-physical attacks such as sabotage and denial of service. The potential security threats emphasize the importance of implementing robust security measures to mitigate the cyber risks to computer numerical control machines.

Forihat, Yahya [Virginia Commonwealth University, ↗