Securing Anomaly Detection for Process-Based Time Series
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22 GENERAL STUDIES OF NUCLEAR REACTORS↗
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East Tennessee Technology Park Biological Monitoring and Abatement Program 2024 Calendar Year Report
The East Tennessee Technology Park (ETTP) Biological Monitoring and Abatement Program (BMAP) consists of three tasks that reflect different but complementary approaches to evaluating the ecological integrity of waters near ETTP. These tasks include (1) bioaccumulation monitoring of fish and clams, (2) benthic macroinvertebrate species richness and density monitoring, and (3) fish community monitoring. The sampling and analysis requirements for the ETTP BMAP in calendar year 2024, covering in part both FY 2024 and FY 2025, are outlined in the respective FY sampling and analysis plans (UCOR 2023, 2024). Sampled water bodies and locations for the ETTP BMAP are shown in Figures 1 and 2. This ETTP BMAP report presents the CY 2024 results and provides context with results from previous years. The report also includes Oak Ridge National Laboratory (ORNL)–generated biological monitoring data collected for other US Department of Energy programs, including the UCOR Water Resources Restoration Program (WRRP) off-site fish bioaccumulation data (UCOR 2023) and select Y-12 National Security Complex (Y-12) BMAP fish bioaccumulation data. Historical data collected for the ETTP BMAP and other programs in the nearby Poplar Creek and Clinch River are provided where appropriate. This progress report provides an update on the biological monitoring activities supporting the ETTP UCOR Environmental Compliance organization, which sponsors the ETTP BMAP. In addition to this internal reporting, ETTP BMAP results are provided in the annual remediation effectiveness reports and the annual site environmental reports, both of which are publicly available. BMAP data are also available to the public via the Oak Ridge Environmental Information System (https://ucor.com/oak-ridge-environmental-information-system-oreis/).
54 ENVIRONMENTAL SCIENCES↗
Fusing Edge Computing with Transport Security by Leveraging the Controller Area Network Transport Security Tracking and Reporting (C-STAR) Unit
Rapid advances in embedded system complexity and capability provides exciting opportunities for transportation security deployment. Manufacturers and developers of these embedded systems continue to provide lower cost and more powerful solutions that can be leveraged by researchers and engineers. Furthermore, deploying these devices at the “edge” of the Internet-of-Things (IoT) infrastructure provides opportunities for highly capable applications in transport security. In an edge computation architecture, the device is co-located at the source of the data in the larger IoT structure – this provides computational capability at the location directly where the data is collected. For shipment transport security, this provides a direct compute node for digestion of data and mitigation actions in real-time. In our application, the vehicle provides a significant amount of this data that can be processed in real-time via the Controller Area Network Transport Security Tracking and Reporting (C-STAR) edge device. Utilization of a computational node located on the vehicle, such as the C-STAR, capitalizes on previously discussed opportunities of edge architectures. In this paper, we will discuss this security solution’s usability, current deployments, and scalability to further applications in transport security. First, we will cover the supported vehicle platforms that can leverage the C-STAR technology. This will be particularly relevant to medium- and heavy-duty vehicles transporting high-risk shipments. Second, we will speak to current deployments of the C-STAR that are ongoing. Finally, we will discuss additional areas for expansion such as maturing the onboard algorithms through continuing collaborations.
Cook, Adian [ORNL] (ORCID:0000000160825395)↗
Adaptable Standards for Discovery, Access, and Usability of Oak Ridge National Laboratory’s Data Portals and Catalogs
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.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Security and Privacy Issues in New 5G and 6G Capabilities
Slides for opening INL hosted panel on Security and Privacy Issues in New 5G and 6G Capabilities in the Security and Privacy of Next-Generation Networks (FutureG) Workshop co-located with NDSS Symposium 2025, San Diego, CA.
5G Security↗
Data-Informed Synthetic Networks of Water Distribution Systems for Resilience Analysis in Puerto Rico
The increasing potential of infrastructure disruptions calls for high-quality infrastructure models to be used in resilience analysis and decision making. Unfortunately, many utilities and communities do not have access to accurate and detailed models due to a lack of data and resources. Furthermore, security restrictions on sharing infrastructure models present roadblocks to research, analysis, and decision making. Recent advances in the development of synthetic water distribution models provide a potential solution to this problem. There is an opportunity to improve these methods by leveraging incomplete pipe datasets to aid synthetic network generation. To address this gap, we developed a methodology for synthetic network generation that incorporates partial pipe data using a modification of the minimum cost flow algorithm for network generation and pipe sizing. This methodology demonstrates how partial pipe data can be leveraged to improve site-specific synthetic network generation. For the study area of Mayagüez, Puerto Rico, a synthetic model generated using 50% of real pipe data matches the pressure of the validation system with an average error of 23.5 m of head, which improves upon the average error of 31.6 m of head produced by a synthetic model generated using no data of the real pipes. Additionally, synthetic networks are shown to replicate the pressure response under a disruption scenario of the validation network, suggesting potential use in resilience analysis.
resilience analysis↗
Integrating AEAD Ciphers into Software-Defined-Storage Systems
The use of software-defined storage (SDS) systems to store sensitive data is becoming increasingly prevalent. However, these systems primarily implement security measures to ensure the confidentiality and availability of stored data, with limited consideration for the protection of its integrity. This paper outlines why this is a harmful development, as well as how integrity-protecting measures can be included into SDS systems. To demonstrate the practical challenges and opportunities of such measures, we integrated "authenticated encryption with associated data" (AEAD) ciphers into the widely used SDS system Ceph, specifically, into its block storage interface, to secure the integrity of stored data and metadata. Ultimately, we identify the characteristics that an SDS system should possess to adopt our methodology.
Mohren, David [University of New Brunswick, Canada↗
FL‐ADS: Federated learning anomaly detection system for distributed energy resource networks
Abstract With the ongoing development of Distributed Energy Resources (DER) communication networks, the imperative for strong cybersecurity and data privacy safeguards is increasingly evident. DER networks, which rely on protocols such as Distributed Network Protocol 3 and Modbus, are susceptible to cyberattacks such as data integrity breaches and denial of service due to their inherent security vulnerabilities. This paper introduces an innovative Federated Learning (FL)‐based anomaly detection system designed to enhance the security of DER networks while preserving data privacy. Our models leverage Vertical and Horizontal Federated Learning to enable collaborative learning while preserving data privacy, exchanging only non‐sensitive information, such as model parameters, and maintaining the privacy of DER clients' raw data. The effectiveness of the models is demonstrated through its evaluation on datasets representative of real‐world DER scenarios, showcasing significant improvements in accuracy and F1‐score across all clients compared to the traditional baseline model. Additionally, this work demonstrates a consistent reduction in loss function over multiple FL rounds, further validating its efficacy and offering a robust solution that balances effective anomaly detection with stringent data privacy needs.
Purohit, Shaurya [Iowa State University Ames Iowa ↗
Developing Data-Driven Synthetic Infrastructure Models for Resilience Analysis
Research on infrastructure resilience has produced promising methods to simulate and optimize complex networks to improve performance. However, restrictions on sharing infrastructure models and the steep cost of developing and maintaining infrastructure models presents a roadblock to adoption. To overcome this limitation, this research focuses on methods to create data-driven infrastructure models that will help improve infrastructure resilience and security. The analysis couples incomplete utility data, geospatial data, machine learning, and synthetic network generation methods to rapidly develop and update infrastructure models. The methods are validated using realistic utility models and site-specific data, with a focus on Puerto Rico due to its unique infrastructure challenges and available data. This research highlights promising opportunities for the use of synthetic network generation and machine learning to create infrastructure models when very little data is available. Results demonstrate that hybrid methods, which combine sparse utility data with synthetic models, can enhance model accuracy, and machine learning can predict model attributes using training data from other models. However, the complexity of infrastructure systems means that even minor changes in network connectivity can significantly impact simulation results. Resilience analysis using synthetic infrastructure models shows that while some system behaviors are preserved, the magnitude of disruptions may not be accurately represented, indicating the need for more research and validation before using synthetic models for critical infrastructure investment decisions. The framework outlined in this report represents a significant advance to infrastructure model development and could be applied to additional domains and sites. Future research will continue to streamline and validate methods to help reduce roadblocks to resilience analysis.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Electric Utility Communications Standards Landscape 2025 Edition
Historically, challenges to managing electric utility data exchange have been addressed through dedicated communication solutions, enabling the transmission of data with both speed and security. Protocols have been deployed in a relatively uniform fashion; for example, field communications for Supervisory Control and Data Acquisition (SCADA) are commonly implemented using IEEE 1815 (DNP3).
24 POWER TRANSMISSION AND DISTRIBUTION↗
FY 2025 Multidimensional Data Correlation Platform: Unified Software Architecture for Advanced Materials and Manufacturing Technologies Data Management and Processing
The Advanced Materials and Manufacturing Technologies (AMMT) program continues to advance a data-driven approach to demonstrate the utility of additive manufacturing for fabricating components for nuclear applications. A key scientific goal is to leverage data to better understand manufacturing outcomes and thereby improve the performance, reliability, and lifespan of nuclear components. Ultimately, this effort supports the development of standards for certification and qualification of additively manufactured components, enabling broader industry adoption. In support of this objective, the AMMT program is building and deploying a data management platform to record, index, analyze, and make available the manufacturing data generated across the AMMT program. In FY 2023, the team conceptualized the architecture of the platform and, in FY 2024, deployed the first functional version at the Oak Ridge National Laboratory (ORNL) Manufacturing Demonstration Facility (MDF). In FY 2025, the platform was officially opened to all AMMT members. To enable this expansion, core modifications and enhancements were developed, including improvements to the user interface and workflows for data entry and retrieval. Most notably, robust security and access control mechanisms were implemented to protect data and manage information sharing. This effort featured a logging system, protected views, and controlled access mechanisms. This report documents these enhancements and the transition of the platform into program-wide use.
36 MATERIALS SCIENCE↗
Reconfigurable Network Slicing Orchestration in Network Function Virtualization Compatible Operational Technology Environment
The ongoing transition to Industry 4.0, which is characterized by increased inter-connectivity of cyber-physical systems, requires having time-sensitive, high throughput, and secure transfer of critical data in industrial sites. In this context, network slicing emerges as a critical tool to ensure timely data delivery by provisioning the network resources to cater to specific applications’ requirements and mitigating potential cyber attacks. To address these challenges, this paper aims to tackle two key questions essential for the successful implementation of network slicing in industrial environments. First, it investigates architectural considerations for developing a network infrastructure capable of supporting network slicing functionalities effectively. The proposed approach significantly improves deployment efficiency over traditional manual configurations. Second, it delves into the automated orchestration process, elucidating the steps and components involved in transitioning from a static network management approach to dynamically leverage network function virtualization schemes for creating network slices in ad-hoc manner. The system demonstrates high throughput suitable for production-level solutions and maintains exceptionally low latency, making it ideal for ultra-reliable low-latency communications. Even with increased network demands, the system remains stable, with effective Quality of Service (QoS) management, ensuring reliable performance under varying conditions. The proposed architecture outlines the necessary components, services, and communication protocols required for a production-level orchestrator for network segmentation in SCADA environments.
Rodiles Delgado, Brian G.↗
Aerosol and Gas Transport in Ventilation Ducts in Nonreactor Nuclear Facilities
This document summarizes outcomes and finding in FY 2022 from a project sponsored by the Nuclear Safety Research and Development Program, which is managed by the Office of Nuclear Safety, within the Office of Environment, Health, Safety and Security. Literature survey and data collection are discussed in Sections 1 and 2, respectively. Numerical modeling of particulate transports in ventilation systems performed for standard geometries and a full-scale ventilation system is described in Section 3, and Section 4 summarizes the development of proof-of-concept sensors featuring ultrasound technology for particle deposition removal. Conclusions and recommendations are outlined in Section 5.
42 ENGINEERING↗
Studies in Nuclear and Nucleon Structure, and Neutrino Physics
We have provided training to students in skill sets relevant to job placement in areas of national needs. These trainings are tuned towards placing graduating students in the workforce related to sciences at national laboratories, national security, information systems, and data sciences. The support for undergraduate students will address the academic retention shortfalls by providing opportunities in mentored research experiences.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Secure Route: Roadway Risk Mapping for Transportation Planners
The secure transport of sensitive materials across U.S. road networks pose unique challenges for local, state, and federal agencies. Threats range from random events (e.g., accidents, medical emergencies, mechanical failures) to opportunistic or organized tactical assaults. Although the probability of such attacks is very low, the consequences of material loss to foreign states or terrorists can be catastrophic, qualifying these scenarios as “grey swan” events—low-probability, high-impact occurrences that are predictable but difficult to quantify. Traditional risk assessments struggle in these contexts, necessitating a shift toward subjective risk perception to inform planning. Risk perception in transport planning is shaped by various factors, including knowledge of adversarial capabilities, vehicle defenses, manifest details, and geographic features along the route. Geographic features such as bridges, tunnels, roadside elevation, and gaps in cellular coverage introduce vulnerabilities, while mitigative features include safe havens, police stations, and medical services. Temporal variables such as congestion, accidents, and weather further complicate route planning. Despite their importance, existing routing tools like Google Maps and commercial software do not explicitly account for geographic risk features, requiring planners to rely on personal familiarity with routes—a time-intensive, non-scalable approach. This work addresses these gaps by: (1) developing datasets that catalog geographic risk features along U.S. roadways, (2) eliciting risk perceptions from experienced transportation security experts, and (3) linking these perceptions to roadway conditions and geographic data. We implement these capabilities within Secure Route a novel mapping tool for classifying route segment risks associated with roadway conditions. This system provides transportation planners with an intuitive interface to assess and contextualize risk along potential routes, improving decision-making for secure transport. We present current progress in this effort and identify next steps.
Stewart, Robert [ORNL] (ORCID:0000000281867559)↗
CLEAP Project: OR-SAGE Analysis for MT, UT, and CO States
The OR-SAGE tool is designed to use industry-accepted practices in screening sites and then employ the proper array of data sources through the considerable computational capabilities of GIS technology available at ORNL. The tool was developed to screen the potential for NPP siting on a national and regional basis. However, because of the tool granularity, it is often focused specifically on the immediate area around user sites of interest. If data center siting parameters can be added to OR-SAGE, the ability to evaluate data center siting on a localized scale will be beneficial.1 More than 60 data sets have been collected and processed by ORNL to develop exclusionary, avoidance, and suitability criteria for screening sites for a variety of power generation types, including nuclear power plants. Available site evaluation parameters include population density, slope, seismic activity, proximity to cooling-water sources, proximity to hazard facilities, avoidance of protected lands and floodplains, susceptibility to landslide hazards, and many others. All siting parameters should be considered as flags to inform siting decisions and should not be used to rule in or rule out any NPP site. Once data center siting parameters are identified, appropriate data sets will be collected and processed. The OR-SAGE process is very versatile. Essentially, OR-SAGE is a visual, relational database. The database partitions the contiguous United States, a total of 720 million hectares (~1.8 billion acres), into 100-m by 100-m (1 hectare or ~2.5 acre) cells. The database is tracking just under 700 million individual land cells. Successive suitability criterion is applied to each cell in the database. User-specified thresholds can be applied to each siting parameter data layer. In this manner, a variety of scenarios can be quickly and thoroughly evaluated. Data can be added and/or revised within OR-SAGE to address user interests. Siting security assessment capability is currently being added to OR-SAGE. Security is expected to be of concern at data centers whether it is collocated with a nuclear power generating technology or not. If data center is collocated with a nuclear power generating source, the security threat attractiveness level of both will likely increase. It will be of additional benefit if a potential data center site is also assessed for security vulnerability.
97 MATHEMATICS AND COMPUTING↗
Countering Weapons of Mass Destruction Office (CWMD) Data Categorization Study: Chemical, Biological, Radiological, and Nuclear (CBRN) Detection Device Data
Pacific Northwest National Laboratory (PNNL) seeks to address critical questions related to chemical, biological, radiological, and nuclear (CBRN) detection devices. This research aims to enhance the security and understanding of these devices by investigating various aspects of their identification, communication, and functionality. The primary focus is on network security, malware detection, device identification, and intelligence gathering. CBRN data can be categorized in various ways depending on the purpose of CBRN detection devices and the specific context of the applications for analysis. Criteria that can be used to assist in this effort include but are not limited to data type, data protocol, source/destination, application, time, security, and content. This study will inform additional paths for data classification, data profiling, data mapping, and data modeling. This will help the Countering Weapons of Mass Destruction Office (CWMD) better understand their data and make informed decisions based on the insights gained from this study and their application. The CBRN Data Categorization study will include the identification of 5–10 different CBRN detection devices with unique characteristics for assessing and analyzing the data that is being produced by and transmitted from these devices.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
Emerging Technologies for Privacy Preservation in Energy Systems
This study explores the intersection of digitalization and privacy within the energy sector, focusing on the emerging challenges and opportunities presented by integrating Distributed Energy Resources (DERs) and advanced metering infrastructure. The need for robust digital privacy measures has become crucial as the energy industry evolves towards a more decentralized, digitalized, and decarbonized future. This study delves into four cutting-edge privacy-preserving technologies—Homomorphic Encryption (HE), Secure Multiparty Computation (SMPC), Differential Privacy (DP), and Federated Learning (FL)—each offering unique solutions to safeguard consumer data by increasing digital connectivity and data exchange. Through a detailed examination of these methods, the study explains how each technology operates, its applications within the energy sector, and the specific privacy challenges it addresses. Homomorphic Encryption allows for secure computations on encrypted data, enabling data analysis without compromising privacy. Secure Multiparty Computation enables collaborative data analysis across different entities while protecting the confidentiality of the inputs. Differential Privacy introduces randomness into the assembled data set, preventing the identification of individual records in statistical databases. Lastly, Federated Learning offers a paradigm shift in data analysis, where machine learning models are trained at the edge, minimizing the centralization of sensitive data. The research underscores the significance of implementing these privacy-enhancing technologies to comply with strict data protection regulations, foster consumer trust, and enhance the security of the energy infrastructure. By providing a comprehensive overview of these methodologies and their practical implications for the energy sector, this study aims to contribute to the ongoing discourse on digital privacy, offering insights into how the energy industry can navigate the complexities of data privacy in the digital age.
Cali, Umit↗