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DOE OSTI2026

Virtual Power Plant Architecture and Resilient Design

Virtual Power Plants (VPPs) represent a fundamental shift in electric grid operations, aggregating distributed energy resources (DERs) such as solar panels and battery storage to deliver utility-scale grid services traditionally provided by centralized power plants. This report examines the unique architectural, operational, and digital assurance considerations that distinguish VPPs from conventional utility infrastructure as they scale from pilot projects to mainstream deployment across the United States. While VPPs offer significant opportunities for grid modernization and enhanced flexibility, their distributed, multi-stakeholder architecture introduces distinct security challenges that differ fundamentally from traditional generation facilities. The analysis identifies risks in VPP operations, including device-level security gaps, platform vulnerabilities, and communication protocol weaknesses that create expanded attack surfaces compared to centralized power plants. Through examination of real-world incidents and emerging threat patterns, the report demonstrates how some VPPs' reliance on consumer-owned devices, public internet infrastructure, and complex vendor ecosystems require new approaches to digital assurance and operational security. The findings provide practical guidance for utilities, regulators, and aggregators to implement robust security frameworks and operational best practices essential for maintaining grid reliability as VPP deployment accelerates under the Federal Energy Regulatory Commission (FERC) Order 2222 and related regulatory initiatives.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1997

Multiple Hub Network Choice in the Liberalized European Market

A key question that so far has received relatively little attention in the germane literature is that of the changes at various airports as a result of the EU liberalization policies. That is, presently, most major European airports still benefit from the so-called home-carrier phenomenon where the country's publicly or semi-publicly owned carrier uses the country's main airport as its gateway hub and, consequently, the home-carrier is also the principal user of this airport (in terms of proportion of total aircraft movements, number of passengers transported, connections, slots ownership, etc.). The country's main airport has substantially benefited from these monopoly conditions of airline captivity, strongly determined by the bilateral system of international air transport regulation. Therefore, European major airports were used to operate in essentially different markets, compared to the increasingly competitive markets of their home based carriers. This partly explains relative stability of transport volumes and financial results of European major airports compared to the relatively volatile financial results of most European national airlines. However, the liberalization of European aviation is likely to change this situation. Market access is open now to all community carriers, i.e. carriers with majority ownership and effective control in the hands of EU citizens. Ticket prices are free, governments can only intervene in case of dumping or excessive pricing. A community airline can choose its seat in any of the 15 member states. Licensing procedures are harmonized between member states. In the last few months community carriers have had unrestricted route access within the EU. Most probably this development will be extended to countries inside and outside Europe. Last year the European Commission got the mandate to start negotiations with 10 other European countries. In the meantime the EC has also started negotiations with the USA on so-called soft rights. In the meantime, open skies agreements have been concluded between the USA and most of the EU member states to facilitate strategic alliances between airlines of the states involved. As a result of this on-going liberalization the model of the single 'national' carrier using the national home base as its single hub for the designated third, fourth and sixth freedom operations will stepwise disappear. Within the EU the concept of the national carrier has already been replaced by that of the community carrier. State ownership in more and more European carriers is reduced. On the longer run mergers or even bankruptcy will further undermine the "single national carrier - single national hub" model in Europe. In the meantime, strategic alliances between national carriers in Europe will already reduce the airlines' loyalty to a single airport. Profit maximization and accountability to share holders will supersede the loyalty of these newly emerging alliances, probably looking for the opportunities of a multiple hub network to adequately cover the whole European market. As a consequence, some European airports might see a substantial decline in arriving, departing and transfer traffic, thus in revenues and financial solvency, as well as in their connection to other inter-continental and intra-European destinations. At the same time, other airports might realize a significant increase in traffic as they will be sought after by the profit maximizing airlines as their major gateway hubs. Which will be the losing airports and which will be the winning ones? Can airports anticipate the actions of airlines in deregulated markets and utilize policies which will improve their relative position? If so, what should be these anticipatory policies? These questions become the more urgent, since an increasing number of major European airports will be privatized in the near future. Although increasing airport congestion in Europe will also be reflected in a growing demand pressure for airport slots, this is not a guarantee for a stable transport volume growth of individual airports. The more volatile the market is, the more vulnerable privatized airports become. Therefore, the main issue of this study is the analysis of the opportunities of major European airports to become a central hub as a result of the network choices made by the new European airlines in a completely liberalized market. In a previous study (Berechman and de Wit, 1996), we already explored the potential of Amsterdam Airport Schiphol of becoming the major West-European hub, once European aviation markets are deregulated. A major hindrance of that study was the use of a single hub-and-spoke network. For example that model could not analyze the viability of different combinations of European hubs within a multiple hub network of alternative airline alliances. In this study, we have formulated the model of a multi-hub network where two West-European airports are used for inter-continental and intra-European travel to enable a more realistic analysis of hub choice. Like the previous one also this multi-hub model is primarily used to assess the potential ability of Amsterdam Airport Schiphol for becoming a major West-European hub. Thus, in particular, the policy tests focus on this airport in a double hub network.

Berechman, Joseph↗

NASA NTRS2009

Information Extraction for System-Software Safety Analysis: Calendar Year 2008 Year-End Report

This annual report describes work to integrate a set of tools to support early model-based analysis of failures and hazards due to system-software interactions. The tools perform and assist analysts in the following tasks: 1) extract model parts from text for architecture and safety/hazard models; 2) combine the parts with library information to develop the models for visualization and analysis; 3) perform graph analysis and simulation to identify and evaluate possible paths from hazard sources to vulnerable entities and functions, in nominal and anomalous system-software configurations and scenarios; and 4) identify resulting candidate scenarios for software integration testing. There has been significant technical progress in model extraction from Orion program text sources, architecture model derivation (components and connections) and documentation of extraction sources. Models have been derived from Internal Interface Requirements Documents (IIRDs) and FMEA documents. Linguistic text processing is used to extract model parts and relationships, and the Aerospace Ontology also aids automated model development from the extracted information. Visualizations of these models assist analysts in requirements overview and in checking consistency and completeness.

Malin, Jane T.↗

NASA NTRS2009

Automation for System Safety Analysis

This presentation describes work to integrate a set of tools to support early model-based analysis of failures and hazards due to system-software interactions. The tools perform and assist analysts in the following tasks: 1) extract model parts from text for architecture and safety/hazard models; 2) combine the parts with library information to develop the models for visualization and analysis; 3) perform graph analysis and simulation to identify and evaluate possible paths from hazard sources to vulnerable entities and functions, in nominal and anomalous system-software configurations and scenarios; and 4) identify resulting candidate scenarios for software integration testing. There has been significant technical progress in model extraction from Orion program text sources, architecture model derivation (components and connections) and documentation of extraction sources. Models have been derived from Internal Interface Requirements Documents (IIRDs) and FMEA documents. Linguistic text processing is used to extract model parts and relationships, and the Aerospace Ontology also aids automated model development from the extracted information. Visualizations of these models assist analysts in requirements overview and in checking consistency and completeness.

Malin, Jane T.↗

NASA NTRS2000

Failure Modes and Effects Analysis (FMEA): A Bibliography

Failure modes and effects analysis (FMEA) is a bottom-up analytical process that identifies process hazards, which helps managers understand vulnerabilities of systems, as well as assess and mitigate risk. It is one of several engineering tools and techniques available to program and project managers aimed at increasing the likelihood of safe and successful NASA programs and missions. This bibliography references 465 documents in the NASA STI Database that contain the major concepts, failure modes or failure analysis, in either the basic index of the major subject terms.

Source record↗

DOE OSTI2023

An Interpretable Index of Social Vulnerability to Environmental Hazards

Index-based measures of social vulnerability to environmental hazards are commonly modeled from composites of population-level risk factors. These models overlook individual context in communities' experiences of environmental hazards, producing metrics that may hinder spatial decision support for mitigating and responding to hazards. This paper introduces an interpretable, high-resolution model for generating an individual-oriented social vulnerability index (IOSVI) for the United States built on synthetic populations that couples individual and social determinants of vulnerability. The IOSVI combines an individual vulnerability index (IVI) that ranks individuals in an area’s synthetic population based on intersecting risk factors, with a social vulnerability index (SVI) based on the population’s cumulative distribution of IVI scores. Interpretability of the IOSVI procedure is demonstrated through examples of national, metropolitan, and neighborhood (census tract) level spatial variation in index scores and IVI themes, as well as an exploratory analysis examining risk factors affecting a specific sub-population (military veterans) in areas of high social and environmental vulnerability.

Tuccillo, Joe↗

DOE OSTI2020

Identification of Worst Impact Zones for Power Grids During Extreme Weather Events Using Q-learning

Both the frequency and intensity of extreme weather events have been trending higher in recent years, leading to significant infrastructure damage in the electric grid. The impact of these extreme weather events is desired to be analyzed and quantified to help transmission and distribution system operators prepare for and prevent significant damage and subsequent loss of power. In this paper, we develop an approach that models the impact of extreme weather on the grid and identifies the worst impact zone using Q-learning (a reinforcement learning approach). The identification results reveal grid vulnerability to weather events and provide insights for system operators to help achieve optimal resource allocation and crew dispatch to minimize the adverse impacts of extreme weather. Simulation studies are conducted on the IEEE 123-node system to demonstrate the performance of the proposed approach.

distribution system↗

NASA NTRS2024

San Joaquin Valley Health & Air Quality II: Assessing Urban Heat Island Distribution and its Intersections with Air Quality to Understand Converging Vulnerabilities

The city of Stockton, California, located within the San Joaquin Valley (SJV), is a major hub for agricultural production and has endured the continuous threat to community health from nitrogen dioxide (NO 2 ) and increasing temperatures. The convergence of these issues occurs within historically segregated communities that are disproportionately facing health risks related to heat and air quality. Little Manila Rising (LMR), a social and environmental justice (EJ) advocacy non-profit, partnered with NASA DEVELOP for a second term project to evaluate county wide urban heat islands, sociodemographic vulnerability, landcover classification, and the convergence of these variables. We utilized Landsat 8 Thermal Infrared Sensor (TIRS) and Operational Land Imager (OLI) data to produce a land surface temperature (LST) and Normalized Difference Vegetation Index map. They added Centers for Disease Control (CDC) socioeconomic data from 2020 to identify which communities in Stockton were more susceptible to these environmental factors. Additionally, we used NAIP imagery to create a landcover map differentiating developed infrastructure from tree canopy cover. We discovered that south Stockton, where LMR resides, had the worst convergence of heat, air pollution, low canopy coverage and sociodemographic vulnerability compared to northern and rural parts of the city. This was further substantiated by statistical analysis showing a strong positive relationship between areas of high LST and low vegetation. The results provided LMR with compelling evidence to use in their EJ advocacy, and in their efforts to inform state officials of the discriminatory issues they face.

Urban Heat Islands↗

NASA NTRS2008

Analysis of Proposed 2007-2008 Revisions to the Lightning Launch Commit Criteria for United States Space Launches

Ascending space vehicles are vulnerable to both natural and triggered lightning. Launches under the jurisdiction of the United States are generally subject to a set of rules called the Lightning Launch Commit Criteria (LLCC). The LLCC protect both the vehicle and the public by assuring that the launch does not take place in conditions posing a significant risk of a lightning strike to the ascending vehicle. Such a strike could destroy the vehicle and its payload, thus causing failure of the mission while releasing both toxic materials and debris. To assure safety, the LLCC are conservative and sometimes they may seriously limit the ability of the launch operator to fly as scheduled even when conditions are benign. In order to safely reduce the number of launch scrubs and delays attributable to the LLCC, the Airborne Field Mill (ABFM) program was undertaken in 2000 - 2001. The effort was directed to collecting detailed high-quality data on the electrical, microphysical, radar and meteorological properties of thunderstorm-associated clouds. The expectation was that this additional knowledge would provide a better physical basis for the LLCC and allow them to be revised to be both safer and less restrictive. That expectation was fulfilled, leading to significant revisions to the LLCC in 2003 and 2005. The 2005 revisions included the application of a new radar-derived quantity called the Volume Averaged Height Integrated Radar Reflectivity (VAHIRR) in the rules governing flight through anvil clouds. Analysis of the ABFM data has continued, and two additional revisions to the LLCC were proposed in late 2006 for adoption in 2007 or 2008. One proposal was to apply the VAHIRR concept to debris clouds, and the other was to reduce the "stand-off distances" in the rules for anvil and/or debris clouds. The stand-off distance is the clearance (out side of the cloud) required between the flight path of the vehicle and the edge of a cloud that it is not permissible to fly through. This paper will discuss these proposed changes in the LLCC and the scientific rationale for adopting or rejecting them based on ABFM data.

Dye, J. E.↗

DOE OSTI2021

Retrospective Analysis and Bayesian Model Averaging of CMIP6 Precipitation in the Nile River Basin

The Nile River basin is one of the global hotspots vulnerable to climate change impacts because of a fast-growing population and geopolitical tensions. Previous studies demonstrated that general circulation models (GCMs) frequently show disagreement in the sign of change in annual precipitation projections. Here, we first evaluate the performance of 20 GCMs from phase six of the Coupled Model Intercomparison Project (CMIP6) benchmarked against a high-spatial-resolution precipitation dataset dating back to 1983 from Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR). Next, a Bayesian model averaging (BMA) approach is adopted to derive probability distributions of precipitation projections in the Nile basin. Retrospective analysis reveals that most GCMs exhibit considerable (up to 64% of mean annual precipitation) and spatially heterogenous bias in simulating annual precipitation. Moreover, it is shown that all GCMs underestimate interannual variability; thus, the ensemble range is underdispersive and is a poor indicator of uncertainty. The projected changes from the BMA model show that the value and sign of change vary considerably across the Nile basin. Specifically, it is found that projected changes in the two headwaters basins, namely, the Blue Nile and Upper White Nile, are 0.03% and -1.65%, respectively; both are statistically insignificant at α = 0.05. The uncertainty range estimated from the BMA model shows that the probability of a precipitation decrease is much higher in the Upper White Nile basin whereas projected change in the Blue Nile is highly uncertain both in magnitude and sign of change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Enabling Cybersecurity, Situational Awareness and Resilience in Distribution Grids with High Penetration of Photovoltaics (CARE-PV) (Final Report)

Since legacy distribution systems have very limited visibility beyond the substation, high penetration of PV at the grid edge presents some unique operational challenges. One approach to address these challenges is to use information from advanced metering infrastructure (AMI) and µPMUs. However, exploiting this information is impacted by a number of factors, including multi-timescale measurements, volume of data generated, communication network impairments (e.g., information loss and latency) and susceptibility to cyber-attacks. Therefore, one of the critical tasks involved in the management of a distribution grid is to develop complete situational awareness by integrating cyber-security mechanisms with state estimation strategies and leveraging this situational awareness to assure energy services at strategic locations while exploiting AMI/PV inverter/ µPMU data. This CARE-PV project addresses the fundamental challenges in situational awareness and resilience to cyber and physical vectors by exploiting the synergy between innovative modeling, estimation, data analytics, testing and validation using smart PV inverters designed at K-State and facilities at NREL. Specifically, the project involved the development, testing and validation of the following novel enabling technologies: (Thrust 1) Resilience to cyber vectors that impact data integrity was addressed via a two-level defense strategy that combines cyber intrusion detection using self-learning, cooperative smart PV inverters, and a novel moving target defense framework to combat data integrity attacks. (Thrust 2) Resilience to cyber-physical vectors that impact situational awareness by limiting data availability was addressed via novel centralized and decentralized, sparsity-based static and dynamic state estimation approaches that enhance observability even when the underlying system is unobservable. (Thrust 3) Leveraging a unique probabilistic sensitivity analysis approach accompanied by one-of-a-kind dominant influencer set computation, the vulnerability of critical infrastructure at strategic locations was evaluated so that proactive PV-based control strategies can be used to support operations under normal/outage scenarios. These CARE-PV project innovations were demonstrated on both small-scale IEEE and larger utility-scale testbeds (Thrust 4). Feedback from Industry Advisory Board members was used to formulate a commercialization pathway for a subset of CARE-PV technologies. These CARE-PV technologies will ultimately lead to reliable and secure, large-scale integration of renewable energy and mitigate the risk of energy disruption resulting from cyber incidents and other emerging threats within the energy environment.

14 SOLAR ENERGY↗

DOE OSTI2022

Microbial Community Structure and Ecological Networks during Simulation of Diatom Sinking

Microbial-mediated utilization of particulate organic matter (POM) during its downward transport from the surface to the deep ocean constitutes a critical component of the global ocean carbon cycle. However, it remains unclear as to how high hydrostatic pressure (HHP) and low temperature (LT) with the sinking particles affects community structure and network interactions of the particle-attached microorganisms (PAM) and those free-living microorganisms (FLM) in the surrounding water. In this study, we investigated microbial succession and network interactions in experiments simulating POM sinking in the ocean. Diatom-derived 13C- and 12C-labeled POM were used to incubate surface water microbial communities from the East China Sea (ECS) under pressure (temperature) of 0.1 (25 °C), 20 (4 °C), and 40 (4 °C) MPa (megapascal). Our results show that the diversity and species richness of the PAM and FLM communities decreased significantly with HHP and LT. Microbial community analysis indicated an increase in the relative abundance of Bacteroidetes at high pressure (40 MPa), mostly at the expense of Gammaproteobacteria, Alphaproteobacteria, and Gracilibacteria at atmospheric pressure. Hydrostatic pressure and temperature affected lifestyle preferences between particle-attached (PA) and free-living (FL) microbes. Ecological network analysis showed that HHP and LT enhanced microbial network interactions and resulted in higher vulnerability to networks of the PAM communities and more resilience of those of the FLM communities. Most interestingly, the PAM communities occupied most of the module hubs of the networks, whereas the FLM communities mainly served as connectors of the modules, suggesting their different ecological roles of the two groups of microbes. These results provided novel insights into how HHP and LT affected microbial community dynamics, ecological networks during POM sinking, and the implications for carbon cycling in the ocean.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2017

NASA-STD-6001B Test 7: Impact of Test Methodology and Detection Advancements on the Obsolescence of Historical Offgas Data

NASA-STD-6001B states "all nonmetals tested in accordance with NASA-STD-6001 should be retested every 10 years or as required by the responsible program/project." The retesting of materials helps ensure the most accurate data are used in material selection. Manufacturer formulas and processes can change over time, sometimes without an update to product number and material information. Material performance in certain NASA-STD-6001 tests can be particularly vulnerable to these changes, such as material offgas (Test 7). In addition, Test 7 analysis techniques at NASA White Sands Test Facility were dramatically enhanced in the early 1990s, resulting in improved detection capabilities. Low level formaldehyde identification was improved again in 2004. Understanding the limitations in offgas analysis data prior to 1990 puts into question the validity and current applicability of that data. Case studies on Super Koropon (Registered trademark) and Aeroglaze (Registered trademark) topcoat highlight the importance of material retesting.

Buchanan, Vanessa D.↗

DOE OSTI2020

Deconstructing the Nuclear Supply Chain Cyber-Attack Surface

The nuclear supply chain cyber-attack surface is a large, complex network of interconnected stakeholders and activities. The global economy has widened and deepened the supply chain resulting in larger numbers of geographically dispersed locations and increased difficulty ensuring the authenticity and security of digital assets. Although the nuclear industry has made significant strides in securing facilities from cyber-attacks, the supply chain remains vulnerable. This paper provides further details on each of the elements in the Digital I&C System Supply Chain Cyber-Attack Surface, including supply chain lifecycle activities, key stakeholders, touchpoints, and attack types. Deconstructing this attack surface provides insights into supply chain threats, vulnerabilities, and consequences. These insights will lead to improvements in cybersecurity supply chain risk analysis, development of new cybersecurity supply chain processes and tools, and enhancement of overall supply chain resilience.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Beyond Binary: Automated PLC Memory Forensics through RGB Image Analysis and Deep Learning

The introduction of Industry 4.0 and the evolution of industrial control systems (ICS) to adopt Internet-based technologies enhanced productivity, but have inadvertently increased their vulnerability to cyber-based malicious attacks. When an ICS system is compromised, security analysts need to identify the root cause quickly to start the recovery process and develop mitigation strategies to safeguard against future instances. Memory forensics is critical in the analysis process to ascertain what occurred. To date, approaches to analyze the persistent memory in ICS devices are limited, and almost nonexistent for volatile memory. This paper proposes an automated methodology, COMA, for PLC memory dump analysis using computer vision and deep learning techniques. Specifically, COMA converts the sequences of bytes in a PLC memory dump to RGB pixels and creates a deep learning model that learns the underlying patterns and features of pre-labeled forensic artifacts in images and segments them into distinct regions. COMA then uses the trained model to automatically segment new memory images and extract forensic artifacts. We evaluate COMA on a Schneider Electric Modicon M221 PLC involving two cyber-based attack scenarios: (i) code injection and (ii) code modification. The empirical results show that COMA can successfully detect attack artifacts in memory dumps in both scenarios.

Asmar Awad, Rima↗

DOE OSTI2025

Model-based Hierarchical Reinforcement Learning for Improved Physical Security Design: A Prototype

Prior work in FY24 developed an adversarial AI agent aid in path analysis of physical protection systems. This agent, trained using a model-based reinforcement learning algorithm, was able to successfully learn the most vulnerable path in facilities. It was able to extend the current state of practice for physical protection design by exhibiting dynamic behavior based on current environmental conditions. Whereas PathTrace largely performs a static, graph-based analysis, the AI agent was able to make decisions based on relative position in the facility, current conditions (was the adversarial agnet discovered?), and proximity to secondary targets. The agent demonstrated some novel capabilities, but had limitations that need to be resolved before it can be used for production purposes. For example, the adversarial agent generalizes poorly and takes a relatively long time to train. Nonetheless, there is still considerable promise for developing the adversarial agent further in order to explore even richer, more dynamic behaviors (e.g., adversary motivations, environmental debris, and more). This work considers a complementary idea; development of a planning agent. The planning agent is envisioned as an auto-complete-like tool that can help accelerate security system design by human experts. The agent would respect existing barriers and sensors placed by a human expert while offering cost-effective suggestions (i.e., implicitly balancing effectiveness with cost) to improve the design. The goal is for this agent to be part of an expert’s toolbox, not to totally upend the current state-of-practice, or to displace human experts. The ultimate goal would be concurrent training of both the adversarial and planning agent together, to learn entirely through self-play. This would represent an entirely new way of performing system deign. We selected a hierarchical, model-based reinforcement learning algorithm to serve as the planning agent. This is an extension of concepts used in the prior FY24 adversarial agent work. There, we had a single agent acting an environment. Here, we have two different sub-agents (policies), working together, to form a complete agent. There is a manager policy, which can select abstract goals on slower time scales, and a worker, which performs primitive actions to reach goals selected by the manager. It is worth noting that this class of algorithm is challenging to work with. From our understanding, our work is one of the first successful uses of model-based reinforcement learning (MBRL) in nuclear energy1 , and likely the first hierarchical model-based reinforcement learning application in nuclear energy. Further, this work is one of the first known attempts to apply AI to perform a design tasks in nuclear energy. Consequently, there were significant implementation challenges and the bulk of the work was focused on successful implementation and algorithm design. The results presented here are very low technology readiness level as a consequence of the lack of related literature, but still represent a significant step forward in the pursuit of applied AI for design.

42 ENGINEERING↗

DOE OSTI2022

Assessing the energy equity benefits of energy storage solutions

Safety, reliability, efficiency, and affordability are no longer the sole tenets of electric grid planning. The evolving social and policy climate have placed new explicit requirements on the electric grid, including resilience, decarbonization, and energy equity and justice. Integrating energy equity strategies into modern grid design is intended to achieve a fair and just distribution of benefits within the energy system. This study aims to characterize the energy equity and community benefits of energy storage systems (ESS) under the following three use-case models: utility ESS that are operated within the distribution system; community-owned ESS; and behind-the-meter ESS that are customer-owned to serve the household. The goal of this energy equity analysis is to characterize the environmental, economic, and social benefits of ESS through applied metrics such as energy burden, energy poverty, energy vulnerability, job creation, and more. A 13.8 kV, 265 node representative feeder corresponding to the hot-humid climate of Louisiana, chosen for its high energy burden, frequent hurricanes and outage events, was used to perform a storage adequacy analysis for six different outage scenarios to determine energy storage access for each use-case. The results of this analysis are then used to characterize both the grid and equity benefits of storage solutions to inform a prioritization framework matching community needs with system preferences for utility planning processes, market regulations, and the wider network of energy system stakeholders.

Energy Equity, energy justice, equity, reliability↗

DOE OSTI2025

Braxton Marlatt Intern Poster

The Internet of Things (IoT) encompasses a vast network of interconnected devices embedded with software, sensors, and network connectivity, enabling data collection and exchange. While IoT technology revolutionizes various industries, it also introduces significant security challenges. This research focuses on enhancing IoT security through the implementation of Zero Trust Architecture concepts, specifically targeting the Network and Device pillars of the Cybersecurity and Infrastructure Security Agency’s Zero Trust Maturity Model. By generating Codified Attack Surfaces (CAS) using custom Structured Threat Information eXpression bundles, this project aims to provide enhanced visibility into network communications, detect vulnerabilities in device firmware, and improve the overall security posture for IoT devices and networks. The methodology involves defining custom STIX schema and objects, collecting data from intra-IoT traffic, external network traffic, and firmware analysis, and automating the conversion and correlation of this data into STIX bundles. The automated generation of attack surfaces offers comprehensive insights into activity, vulnerabilities, and anomalies within an IoT environment, enabling proactive threat identification and mitigation.

24 - POWER TRANSMISSION AND DISTRIBUTION↗