Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Threat Detection”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Analysis of passive acoustic ranging of helicopters from the joint acoustic propagation experiment

For more than twenty years, personnel of the U.S.A.E. Waterways Experiment Station (WES) have been performing research dealing with the application of sensors for detection of military targets. The WES research has included the use of seismic, acoustic, magnetic, and other sensors to detect, track, and classify military ground targets. Most of the WES research has been oriented toward the employment of such sensors in a passive mode. Techniques for passive detection are of particular interest in the Army because of the advantages over active detection. Passive detection methods are not susceptible to interception, detection, jamming, or location of the source by the threat. A decided advantage for using acoustic and seismic sensors for detection in tactical situations is the non-line-of-sight capability; i.e., detection of low flying helicopters at long distances without visual contact. This study was conducted to analyze the passive acoustic ranging (PAR) concept using a more extensive data set from the Joint Acoustic Propagation Experiment (JAPE).

Carnes, Benny L.↗
DOE OSTI2024

Safe Operations at Roadway Junctions: Intelligent Roadway Infrastructure as Functional Interlocking

Automated vehicle (AV) technology is quickly maturing, and the corresponding infrastructure systems that evaluate traffic and communicate to vehicles requires sophisticated sensing and perception technologies, referred to as intelligent roadway infrastructure (IRI), to complement emerging AV capabilities. IRI provides signals to vehicles, indicating right-of-way for vehicles and communicating to approaching AVs that no other vehicle is failing to yield. This capability, denoted as safety-affirmative signaling, provides a green light or a green arrow as appropriate and affirms through communication links to connected vehicles when it is safe to proceed. About 36% of collisions occur at intersections, with most occurring upon left turns (22.2%) or crossing over (12.6%), and only a small percentage (1.2%) while turning right at an intersection. Of all intersection crashes about half (52.5%) of those vehicles were traveling through a signalized intersection 2. Safety-affirmative signaling would guarantee safety of AV fleet vehicles, by providing the interlocking principle, a term from automated train control that only allows progression through a railway intersection after affirming no opportunity for a crash exists. IRI through safety-affirmative signaling would bring performance and safety to complex roadway intersections where AV transit fleet service is most needed, as well as safety benefits to traditional, non-automated vehicles and vulnerable road users. The implementation of IRI has functional, programmatic, and technical challenges. Research work performed at the National Renewable Energy Laboratory (NREL) in an integrative approach encapsulating these themes, and termed infrastructure perception and control (IPC) is motivated by improved performance (travel time), improved safety (reduced collisions), and improved energy efficiency (less fuel burned and minimized production of greenhouse gases). IPC is intended not only for roadway and intersection applications but also in extension to inform complementary buildings and grid systems to enable better co-management, as vehicles and their charging needs become increasingly integrated into the built environment. The NREL IPC project presents an open-source framework, architecture, and supporting technology to implement IRI, addressing critical issues such as fusion of data, reliability, standardization of data interfaces, and confidence of detection. The framework is informed by previous experience in U.S. Department of Defense research technology, specifically in the use of radar to detect, identify, and track aerial threats. These principles combined with multi-sensor fusion provides for a complete digital twin with known and measurable confidence and accuracy from which safety-affirmative signaling can be developed and deployed.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2024

A Hybrid Anomaly Detection Approach for Obfuscated Malware

With the rapid evolution of malicious software, cyber threats have become increasingly sophisticated, employing advanced obfuscation techniques to evade traditional detection methods. This study presents a hybrid anomaly detection approach applied to obfuscated malware. Even though there is a large body of research in this field, existing malware detection techniques have some drawbacks, such as requiring large amounts of data, trustworthiness (imprecise results) of algorithms, and advanced obfuscation. To overcome these challenges, there is a need to employ solid and efficient techniques for malware detection. This paper proposes a hybrid approach, combining an autoencoder with traditional machine-learning methods to create an efficient malware detection framework. We used the malware memory dataset (MalMemAnalysis-2022) to evaluate this framework. The results indicate that our proposed approach can detect obfuscated malware when a deep autoencoder used for feature learning is combined with logistic regression, and it is extremely fast with an Accuracy, Detection Rate (DR), Matthew Correlation Coefficient(MCC), and Statistical Parity Difference

malware detection, Hybrid Anomly Detection, Obfusc↗
NASA NTRS2010

Use of Current 2010 Forest Disturbance Monitoring Products for the Conterminous United States in Aiding a National Forest Threat Early Warning System

This presentation discusses contributions of near real time (NRT) MODIS forest disturbance detection products for the conterminous United States to an emerging national forest threat early warning system (EWS). The latter is being developed by the USDA Forest Service s Eastern and Western Environmental Threat Centers with help from NASA Stennis Space Center and the Oak Ridge National Laboratory. Building off work done in 2009, this national and regional forest disturbance detection and viewing capability of the EWS employs NRT MODIS NDVI data from the USGS eMODIS group and historical NDVI data from standard MOD13 products. Disturbance detection products are being computed for 24 day composites that are refreshed every 8 days. Products for 2010 include 42 dates of the 24 day composites. For each compositing date, we computed % change in forest maximum NDVI products for 2010 with respect to each of three historical baselines of 2009, 2007-2009, and 2003-2009,. The three baselines enable one to view potential current, recent, and longer term forest disturbances. A rainbow color table was applied to each forest change product so that potential disturbances (NDVI drops) were identified in hot color tones and growth (NDVI gains) in cold color tones. Example products were provided to end-users responsible for forest health monitoring at the Federal and State levels. Large patches of potential forest disturbances were validated based on comparisons with available reference data, including Landsat and field survey data. Products were posted on two internet mapping systems for US Forest Service internal and collaborator use. MODIS forest disturbance detection products were computed and posted for use in as little as 1 day after the last input date of the compositing period. Such products were useful for aiding aerial disturbance detection surveys and for assessing disturbance persistence on both inter- and intra-annual scales. Multiple 2010 forest disturbance events were detected across the nation, including damage from ice storms, tornadoes, caterpillars, bark beetles, and wildfires. This effort enabled improved NRT forest disturbance monitoring capabilities for this nation-wide forest threat EWS.

Spruce, Joseph P.↗
NASA NTRS1991

Robust Kalman filter design for predictive wind shear detection

Severe, low-altitude wind shear is a threat to aviation safety. Airborne sensors under development measure the radial component of wind along a line directly in front of an aircraft. In this paper, optimal estimation theory is used to define a detection algorithm to warn of hazardous wind shear from these sensors. To achieve robustness, a wind shear detection algorithm must distinguish threatening wind shear from less hazardous gustiness, despite variations in wind shear structure. This paper presents statistical analysis methods to refine wind shear detection algorithm robustness. Computational methods predict the ability to warn of severe wind shear and avoid false warning. Comparative capability of the detection algorithm as a function of its design parameters is determined, identifying designs that provide robust detection of severe wind shear.

Stratton, Alexander D.↗
DOE OSTI2024

Enhancing Network Anomaly Detection Using Graph Neural Networks

In the world of Internet of Things (IoT) networks, where devices are constantly communicating, keeping them secure from cyber threats is critical. This paper introduces a novel approach to detecting unusual and potentially harmful activities in these networks using graph neural networks (GNNs). We combine two specific types of GNNs-GraphSAGE and graph attention networks (GAT)-to create a model that understands and represents the behaviors and interactions in a network. GraphSAGE creates an embedding of network activities by examining local data interactions, while GAT directs the model's focus to the most critical interactions. By integrating these two methods in a single model that considers different types of interactions (both host and flow nodes), we aim to create a system that accurately represents the current state of a network and can also spot anomalies effectively while reducing false positives and negatives. Our innovative approach has demonstrated promising results, achieving an accuracy of 98% on the UNSW-NB15 dataset, significantly outperforming standalone GraphSAGE and GAT models. This underscores its potential as a robust framework for securing IoT networks against cyber threats and anomalies.

Marfo, William↗
DOE OSTI2025

Threat Hunt Guide for BESS Environments

The rapid digitalization of the electric grid - driven by the integration of inverter-based resources (IBRs), battery energy storage systems (BESS), and advanced grid control platforms - has significantly enhanced grid efficiency, visibility, and flexibility. However, this evolution also introduces new cybersecurity risks, particularly through supply chain dependencies and operational blind spots at the grid edge. To address these challenges, Idaho National Laboratory (INL), through the Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response (CESER) Rapid Risk initiative, conducted a series of rapid risk assessment engagements with energy organizations across the United States. Drawing on lessons learned from these engagements, INL developed the following threat hunting guide for asset owners and operators (AOOs) to enhance their cybersecurity visibility within BESS and IBR systems. The guide demonstrates how to use passive network monitoring to baseline device behavior, detect adversarial activity, and investigate anomalies without disrupting operations. By implementing these practices, energy sector stakeholders can improve coordination between cybersecurity and operations teams and strengthen the resilience of distributed energy resources (DERs) within the modern power grid. Prior to implementing any network monitoring, packet capture, or threat hunting activity described in this guide, AOOs are strongly advised to review applicable governance frameworks, legal requirements, and organizational policies. This guide is intended for informational and educational purposes only. It does not replace compliance with any federal, state, or local cybersecurity mandates or industry standards. Implementation of described configurations, technologies, or analytic workflows is performed at the discretion and responsibility of the asset owner and operator.

25 - ENERGY STORAGE↗
DOE OSTI2025

Securing Smart Manufacturing: Detection of Cyber-Physical Attacks in CNC-Based Systems

As Industry 4.0 advances, the integration of computer numerical control (CNC) machines and advanced manufacturing technologies is transforming production into smart manufacturing systems that blend physical and digital processes as cyber-physical systems. However, this increased cyber-physical connectivity exposes manufacturing systems to cyber threats that can cause severe operational and financial disruptions. This paper presents a comparative study on cyber attacks and anomaly detection techniques in manufacturing, focusing on network traffic from CNC machines. The data extracted from network packets includes machine commands and control signals exchanged between the machine's interface and control system, crucial for maintaining operational integrity. We explore two types of cyber attacks, design modification and command injection, which pose substantial risks to CNC machine productivity and system integrity. Our investigation involves experiments on a real CNC system, highlighting the urgent need for effective detection mechanisms. To address these threats, we evaluate three anomaly detection methods: dynamic time warping (DTW), rolling average, and a deep learning, long short-term memory (LSTM) time-series-based autoencoder. Each is assessed for its effectiveness in identifying anomalous behaviors caused by the attacks. Our findings demonstrate the unique strengths and limitations of each detection technique, providing a deeper understanding of their applicability in realworld manufacturing environments. The comparative analysis indicates that while certain methods are highly effective against specific attack types, others offer broader applicability across different attacks. This study contributes to the accurate detection of anomalies in CNC machining processes, thereby enhancing the reliability and security of smart manufacturing systems against diverse cyber threats.

Williams, Bethanie [Tennessee Technological Univer↗
DOE OSTI2025

Reversible parts-per-trillion-level detection of perfluorooctane sulfonic acid in tap water using field-effect transistor sensors

Widespread, persistent and toxic per- and polyfluoroalkyl substances (PFAS) pose a major threat to water systems and human health. Current detection methods are relatively expensive, slow and complex, underscoring the need for more accessible alternatives to meet increasingly stringent PFAS regulations. Here, in this study, we present an ultrasensitive sensing platform for perfluorooctane sulfonic acid detection in tap water with a reporting limit ( ~ 250 parts per quadrillion) lower than the US Environmental Protection Agency’s regulatory standard (4 parts per trillion), using a remote gate field-effect transistor featuring β-cyclodextrin (β-CD)-modified reduced graphene oxide as the sensing membrane. The sensor exhibits excellent selectivity against common inorganic ions, natural organic matter and select organic pollutants in tap water. The reversible and rapid response ( < 2 min) indicates the potential of remote gate field-effect transistor for continuous in-line monitoring. Mechanistic studies using quartz crystal microbalance and molecular dynamics simulations reveal key roles of analyte adsorption and charge properties in sensing performance and offer insights for designing more selective PFAS capture probes.

Wang, Yuqin [University of Chicago, IL (United Sta↗
NASA NTRSDate not supplied

The Influence of Smoke Particle Properties and Cabin Characteristics on Smoke Detection in Lunar Gravity

Spacecraft fires pose a threat to the success of future Lunar, Martian, and deep space exploration missions. As NASA plans to return humans to the Moon in the next decade, novel mission requirements will present new fire safety challenges. For example, materials that are fire resistant on Earth are expected to burn under planned habitat conditions (elevated oxygen concentrations and reduced cabin pressure) and partial gravity (0.16g). Optimal smoke detector placement will depend on a combination of buoyant plume velocities, induced by partial gravity, and Environmental Control and Life Support Systems (ECLSS) parameters, including particle filtration rates, supply and return placement within the cabin, and forced air velocities. These ECLSS parameters must also address the need for rapid Lunar dust removal, as Lunar dust exposure poses a risk to crew health and hardware functionality. Here, we present progress toward a computational fluid dynamics model to evaluate smoke transport in a Lunar habitat. Recent work has demonstrated that if air supplies are placed on ceilings and returns on the floor, a buoyant smoke layer at the ceiling may disperse over the order of minutes even under low forced flow conditions. We expand upon these results to examine the influence of different supply and return configurations on smoke plume development. Additionally, we also address differences in the transport of smoke particles and Lunar dust by varying particle parameters like size, density, and shape factor. Finally, we discuss ongoing and future experimental efforts to measure smoke particle properties and transport under partial gravity, elevated oxygen, and reduced pressure conditions.

Claire Fortenberry↗
DOE OSTI2025

MCNP® Code Version 6.3.1 Release Notes

The Monte Carlo N-Particle® (MCNP® ) code is a general-purpose, continuous-energy, generalized-geometry, time-dependent, radiation transport code developed by the MCNP development team. MCNP calculations provide predictive capabilities that can replace expensive or impossible-to-perform experiments. Specific application problems include simulations of experimental diagnostics, intrinsic radiation, radiation detection and measurement, criticality safety, nuclear threat reduction and response, radiation health protection, nuclear weapons effects, and nuclear forensics. This MCNP code, version 6.3.1, follows the MCNP6.3.0 version. Since the release of MCNP6.3.0, a variety of bug fixes and code enhancements have been completed for MCNP6.3.1. A few new features have also been added to this release to support both ongoing research and the release of the latest ENDF/B-VIII.1 nuclear data library. The MCNP code, version 6.3.1, theory and user input information is documented in MCNP® Code Version 6.3.1 Theory & User Manual, the build guidance for various platforms is documented in MCNP® Code Version 6.3.1 Build Guide, and the verification and validation testing for various application benchmark test suites is documented in MCNP® Code Version 6.3.1 Verification & Validation Testing.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1990

Windshear detection and avoidance - Airborne systems survey

Functional requirements for airborne windshear detection and warning systems are discussed in terms of the threat posed to civil aircraft operations. A preliminary set of performance criteria for predictive windshear detection and warning systems is defined. Candidate airborne remote sensor technologies based on microwave Doppler radar, Doppler laser radar (lidar), and infrared radiometric techniques are discussed in the context of overall system requirements, and the performance of each sensor is assessed for representative microburst environments and ground clutter conditions. Preliminary simulation results demonstrate that all three sensors show potential for detecting windshear, and provide adequate warning time to allow flight crews to avoid the affected area or escape from the encounter. Radar simulation and analysis show that by using bin-to-bin automatic gain control, clutter filtering, limited detection range, and suitable antenna tilt management, windshear from wet microbursts can be accurately detected. Although a performance improvement can be obtained at higher radar frequency, the baseline X-band system also detected the presence of windshear hazard for a dry microburst. Simulation results of end-to-end performance for competing coherent lidar systems are presented.

Bowles, Roland L.↗
NASA NTRS1999

Studies of Florida Thunderstorms Using LDAR, LLP, and Single Doppler Radar Data

The paper summarizes results from research conducted on thunderstorms in the vicinity of the Kennedy Space Center (KSC) Florida, between 1993 and 1998. The focus of the research was to identify procedures that would assist weather forecasters at the Cape Canaveral Air Station (CCAS) in real-time detection and forecasting of the lightning threat to launches and daily ground operations at KSC/CCAS sites. The research was divided into three topics: (1) studies aimed at improving the forecasting of the initial cloud-ground (CG) lightning threat, (2) studies aimed at improving the forecasting of the end-of-storm termination of the CG lightning threat, and (3) studies of the location of CG strikes relative to the thunderstorm radar echo and to lightning discharges aloft. Only the first two topics are covered in this preprint.

Forbes, Gregory S.↗
NASA NTRS2008

Assessing MODIS-based Products and Techniques for Detecting Gypsy Moth Defoliation

The project showed potential of MODIS and VIIRS time series data for contributing defoliation detection products to the USFS forest threat early warning system. This study yielded the first satellite-based wall-to-wall 2001 gypsy moth defoliation map for the study area. Initial results led to follow-on work to map 2007 gypsy moth defoliation over the eastern United States (in progress). MODIS-based defoliation maps offer promise for aiding aerial sketch maps either in planning surveys and/or adjusting acreage estimates of annual defoliation. More work still needs to be done to assess potential of technology for "now casts"of defoliation.

Spruce, Joseph P.↗
NASA NTRS1988

Windshear avoidance - Requirements and proposed system for airborne lidar detection

A generalized windshear hazard index is derived from considerations of wind conditions and an aircraft's present and potential altitude. Based on a systems approach to the windshear threat, lidar appears to be a viable methodology for windshear detection and avoidance, even in conditions of moderately heavy precipitation. The airborne CO2 and Ho:YAG lidar windshear detection systems analyzed can each give the pilot information about the line-of-sight component of windshear threat from his present position to a region extending 1 to 3 km in front of the aircraft. This constitutes a warning time of 15 to 45 s. The technology necessary to design, build and test such a brassboard 10.6-micron CO2 lidar is at hand.

Targ, Russell↗
NASA NTRS1990

Investigation of airborne lidar for avoidance of windshear hazards

A generalized windshear hazard index is defined, which is derived from considerations of wind conditions at the present position of an aircraft and from remotely sensed information along the extended flight path. Candidate airborne sensor technologies based on microwave Doppler radar, Doppler lidar, and infrared radiometric techniques are discussed in the context of overall system functional requirements. Initial results of a performance and technology assessment study for competing lidars are presented. Based on a systems approach to the windshear threat, lidar appears to be a viable technology for windshear detection and avoidance, even in conditions of moderately heavy precipitation. The proposed airborne CO2 and Ho:YAG lidar windshear-detection systems analyzed here can give the pilot information about the line-of-sight component of windshear threat from his present position to a region extending 1 to 3 km in front of the aircraft. This constitutes a warning time of 15 to 45 seconds. The technology necessary to design, build, and test such a brassboard 10.6 micron CO2 lidar is now available. However, for 2-micron systems, additional analytical and laboratory investigations are needed to arrive at optimum 2-micron rare-earth-based laser crystals.

Targ, Russell↗
NASA NTRS2011

Carbon Nanotube Based Nanotechnology for NASA Mission Needs and Societal Applications

Carbon nanotubes (CNT) exhibit extraordinary mechanical properties and unique electronic properties and therefore, have received much attention for more than a decade now for a variety of applications ranging from nanoelectronics, composites to meeting needs in energy, environmental and other sectors. In this talk, we focus on some near term potential of CNT applications for both NASA and other Agency/societal needs. The most promising and successful application to date is a nano chem sensor at TRL 6 that uses a 16-256 sensor array in the construction of an electronic nose. Pristine, doped, functionalized and metal-loaded SWCNTs are used as conducting materials to provide chemical variation across the individual elements of the sensor array. This miniaturized sensor has been incorporated in an iPhone for homeland security applications. Gases and vapors relevant to leak detection in crew vehicles, biomedical, mining, chemical threats, industrial spills and others have been demonstrated. SWCNTs also respond to radiation exposure via a change in conductivity and therefore, a similar strategy is being pursued to construct a radiation nose to identify radiation sources (gamma, protons, neutrons, X-ray, etc.) with their energy levels. Carbon nanofibers (CNFs) grown using plasma enhanced CVD typically are vertical, individual, freestanding structures and therefore, are ideal for construction of nanoelectrodes. A nanoelectrode array (NEA) can be the basis for an affinity-based biosensor to meet the needs in applications such as lab-on-a-chip, environmental monitoring, cancer diagnostics, biothreat monitoring, water and food safety and others. A couple of demonstrations including detection of e-coli and ricin will be discussed. The NEA is also useful for implantation in the brain for deep brain stimulation and neuroengineering applications. Miniaturization of payload such as science instrumentation and power sources is critical to reduce launch costs. High current density (greater than 100 mA/per square centimeters) field emission capabilities of CNTs can be exploited for construction of electron gun for electron microscopy and X-ray tubes for spectrometers and baggage screening. A CNT pillar array configuration has been demonstrated, not only meeting the high current density needs but more importantly providing long term emitter stability. Finally, supercapacitors hold the promise to combine the high energy density of a battery with the high power density of capacitors. Traditional graphite electrodes have not delivered this promise yet. A novel design and processing approach using MWCNTs has shown a record 550 F/g capacitance along with significant device endurance. This supercapacitor is suitable for railgun launch application for NASA, powering rovers and robots, consumer electronics and future hybrid vehicles.

Li, Jing↗
DOE OSTI2026

Vulcan-Forge: Architecture and Design of a Multi-Modal Forensic Analysis Plugin for CALDERA

Forge and VULCAN together describe an open-architecture cybersecurity analysis ecosystem that unifies forensic artifact processing, detection engineering, and vulnerability intelligence within integrated platforms. Forge operates as a plugin for MITRE CALDERA, ingesting diverse evidence formats—including EVTX, PCAP/PCAPNG, CSV, JSON, YAML, XML, binaries, and archives—to construct a unified artifact graph enriched with severity scoring, TLP classification, and audit trails. It provides subsystems for artifact parsing, streaming structured-data visualization, NetworkMiner-based packet inspection, PE/.NET binary analysis, and LLM-assisted triage and rule generation, with outputs validated against CCCS-YARA and pySigma schemas. VULCAN complements this by serving as a cybersecurity analyst platform that integrates a Neo4j knowledge graph, Qdrant vector retrieval, SSVC-based triage, and a local LLM to deliver CVE intelligence and forensic analysis through a multi-source ingest pipeline drawing from NVD, CISA KEV, EPSS, MITRE ATT&CK, and CAPEC. Together, they bridge structured threat intelligence with automated forensic analysis and detection workflows.

97 MATHEMATICS AND COMPUTING↗