MMODS: Detecting Threats Beyond the Limits of Human Sensor Sight (GeoINT Symposium 2025)
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Ma, Tian J. [Sandia National Laboratories (SNL-NM)↗
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Chemical-Sensing Cables Detect Potential Threats
Intelligent Optical Systems Inc. (IOS) completed Phase I and II Small Business Innovation Research (SBIR) contracts with NASA's Langley Research Center to develop moisture- and pH-sensitive sensors to detect corrosion or pre-corrosive conditions, warning of potentially dangerous conditions before significant structural damage occurs. This new type of sensor uses a specially manufactured optical fiber whose entire length is chemically sensitive, changing color in response to contact with its target, and demonstrated to detect potentially corrosive moisture incursions to within 2 cm. After completing the work with NASA, the company received a Defense Advanced Research Projects Agency (DARPA) Phase III SBIR to develop the sensors further for detecting chemical warfare agents, for which they proved just as successful. The company then worked with the U.S. Department of Defense (DoD) to fine tune the sensors for detecting potential threats, such as toxic industrial compounds and nerve agents. In addition to the work with government agencies, Intelligent Optical Systems has sold the chemically sensitive fiber optic cables to major automotive and aerospace companies, who are finding a variety of uses for the devices. Marketed under the brand name Distributed Intrinsic Chemical Agent Sensing and Transmission (DICAST), these unique continuous-cable fiber optic chemical sensors can serve in a variety of applications: Corrosive-condition monitoring, aiding experimentation with nontraditional power sources, as an economical means of detecting chemical release in large facilities, as an inexpensive "alarm" systems to alert the user to a change in the chemical environment anywhere along the cable, or in distance-resolved optical time domain reflectometry systems to provide detailed profiles of chemical concentration versus length.
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PoL Badge: Detecting Insider Threat Activities as well as Deviation from Workplace Patterns-of-Life (PoL) in Nuclear Reactor Facilities using Affordable Privacy-preserving Smart Badging
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De, Debraj [ORNL]↗
Responsible Artificial Intelligence for Insider Threat Mitigation
This report examines the application of artificial intelligence (AI) technologies for insider threat mitigation (ITM) programs in nuclear security facilities. Insider threat detection presents unique challenges due to the subtle and adaptive nature of these threats, the complex signatures involved, and the scarcity of available data for analysis. Traditional human-centered approaches, while essential, face limitations in processing large amounts of data continuously and detecting subtle patterns across multiple systems. AI technologies can potentially address these limitations by providing 24/7 monitoring capabilities, identifying complex patterns that might escape human observation, and offering consistent application of security criteria. However, the deployment of AI in nuclear security contexts introduces significant new risks, including workflow disruption, expanded attack surfaces, potential for misuse, and ethical concerns regarding privacy, fairness, transparency, safety, and security. The high-consequence nature of nuclear security decisions demands careful consideration of these risks and systematic approaches to their mitigation.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
R2U2: Monitoring and Diagnosis of Security Threats for Unmanned Aerial Systems
We present R2U2, a novel framework for runtime monitoring of security properties and diagnosing of security threats on-board Unmanned Aerial Systems (UAS). R2U2, implemented in FPGA hardware, is a real-time, REALIZABLE, RESPONSIVE, UNOBTRUSIVE Unit for security threat detection. R2U2 is designed to continuously monitor inputs from the GPS and the ground control station, sensor readings, actuator outputs, and flight software status. By simultaneously monitoring and performing statistical reasoning, attack patterns and post-attack discrepancies in the UAS behavior can be detected. R2U2 uses runtime observer pairs for linear and metric temporal logics for property monitoring and Bayesian networks for diagnosis of security threats. We discuss the design and implementation that now enables R2U2 to handle security threats and present simulation results of several attack scenarios on the NASA DragonEye UAS.
Formal Methods↗
POWER DATA PIPELINE
SF-25-081 Utility software for creating high-performance data pipelines to extract, load, and transform raw electric power systems measurements. For use with anomaly detection models training workflows. The software supports the project: Adaptive Cybersecurity for DER: A Game-Theoretic and Machine Learning approach for Real-Time Threat Detection and Mitigation
Plathottam, Silby Jose [Argonne National Laborator↗
Optimal Fragmentation and Dispersion of Hazardous Near-Earth Objects
The complex problem of protecting the Earth from the possibility of a catastrophic impact by a hazardous near-Earth object (NEO) has been recently reassessed in [1]. In a letter on NEOs from the White House Office of Science and Technology Policy (OSTP) to the U.S. Senate and Congress in 2010, the White House OSTP strongly recommended that NASA take the lead in conducting research activities for NEO detection, characterization, and deflection technologies. Furthermore, President Obama's new National Space Policy specifically directs NASA to "pursue capabilities, in cooperation with other departments, agencies, and commercial partners, to detect, track, catalog, and characterize NEOs to reduce the risk of harm to humans from an unexpected impact on our planet." The Planetary Defense Task Force of the NASA Advisory Council also recommended that the NASA Office of the Chief Technologist (OCT) begin efforts to investigate asteroid deflection techniques. With national interest growing in the United States, the NEO threat detection and mitigation problem was recently identified as one of NASA's Space Technology Grand Challenges. An innovative solution to NASA's NEO Impact Threat Mitigation Grand Challenge problem was developed through a NIAC Phase I study (9/16/11 - 9/15/12), and it will be further investigated for a NIAC Phase II study (9/10/12 - 9/9/14). Various NEO deflection technologies, including nuclear explosions, kinetic impactors, and slow-pull gravity tractors, have been proposed and examined during the past two decades. Still, there is no consensus on how to reliably deflect or disrupt hazardous NEOs in a timely manner. It is expected that the most probable mission scenarios will have a mission lead time much shorter than 10 years, so the use of nuclear explosives becomes the most feasible method for planetary defense. Direct intercept missions with a short warning time will result in arrival closing velocities of 10-30 kilometers per second with respect to the target asteroid. Given such a large arrival delta V requirement, a rendezvous mission to the target asteroid is infeasible with existing launch vehicles. Furthermore, state-of-the-art penetrating subsurface nuclear explosion technology limits the penetrator's impact velocity to less than approximately 300 meters per second because higher impact velocities prematurely destroy the nuclear fuzing mechanisms. Therefore, significant advances in hypervelocity nuclear interceptor/ penetrator technology must be achieved to enable a last-minute nuclear disruption mission with intercept velocities as high as 30 kilometers per second. Consequently, a HAIV (Hypervelocity Asteroid Intercept Vehicle) mission architecture (Figure 1.1), which blends a hypervelocity kinetic impactor with a subsurface nuclear explosion for optimal fragmentation and dispersion of hazardous NEOs, has been developed through a Phase I study, and it will be further developed and validated through a Phase II study.
Asteroid↗
Capability Building Progression of an Insider Threat Mitigation Program at an International Research Reactor
The nuclear industry recognizes the difficulties involved in developing effective managerial and leadership skills in a highly technical and proficient workforce such as that found in nuclear facilities. Implementing an insider threat mitigation program (ITMP) within the nuclear industry is a complex and ongoing process that demands a comprehensive understanding of human behavior, an organization’s security culture, and rigorous regulatory requirements yet also accounts for facility characteristics, physical security, material flow, and activities involving nuclear material. Given the high-consequence nature of research reactor operations, even minor lapses can lead to safety, security, and reputational risks. An effective ITMP requires a defense-in-depth approach that incorporates behavioral analysis, robust vetting procedures, continuous monitoring, and cross-disciplinary coordination. It must also promote a culture of vigilance and accountability at all levels up to and including executive leadership but be flexible enough to adapt to evolving global threats and technological advances. Insider threat mitigation is not a one-time effort but rather a sustained commitment to excellence in safety and security. Establishing a culture in which personnel proactively report incidents and issues that could affect nuclear safety and security is vital to maintaining a safe and secure operational environment. This document was developed to guide senior management and research reactor organizations in creating comprehensive programs to effectively manage and mitigate insider threat behaviors and actions. It focuses on the key pillars of an effective ITMP, including the national legal framework, security culture, preventive and protective measures, cyber security, and performance evaluation. By using a systematic approach during implementation, facilities can foster environments conducive to insider threat detection and support long-term program sustainability. The document also provides strategies for improving communication across all levels of an organization, helping to eliminate barriers that hinder the development of robust ITMPs and enhance overall security culture. In today’s organizations, the concept of leveraging safety and security culture lessons to facilitate knowledge transfer is rapidly evolving to expedite insider threat management and security culture improvements. This document outlines the rationale for evaluating an ITMP based on national customs, culture, and stakeholders. The elements are all germane to reliability and trustworthiness and relate to security concerns that states may encounter. The document focuses not only on individual perceptions regarding security issues and capability building but also on team building and how to resolve concerns. The implementers of a facility’s ITMP may zero in on indicators of insider threats within their enterprise. This material will benefit organizations when it is applied using a systematic and structured approach as demonstrated throughout the document.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
SSI-ARC Flight Test 3 Data Review
The "Unmanned Aircraft System (UAS) Integration into the National Airspace System (NAS)" Project conducted flight test program, referred to as Flight Test 3, at Armstrong Flight Research Center from June - August 2015. Four flight test days were dedicated to the NASA Ames-developed Detect and Avoid (DAA) System referred to as Autoresolver. The encounter scenarios, which involved NASA's Ikhana UAS and a manned intruder aircraft, were designed to collect data on DAA system performance in real-world conditions and uncertainties with four different surveillance sensor systems. Resulting flight test data and analysis results will be used to evaluate the DAA system performance (e.g., trajectory prediction accuracy, threat detection) and to add fidelity to simulation models used to inform Minimum Operating Performance Standards (MOPS) for integrating UAS into routine NAS operations.
detect and avoid↗
OR22-Neuromorphic Rad Detector-PD3Ra (Final Report)
In unattended monitoring scenarios, automated radiation detection algorithms must be able to detect low signal-to-noise ratio (SNR) anomalies in a potentially dynamic and noisy background and report these anomalies in a timely fashion. Dynamic and noisy backgrounds complicate the use of simple gross-counting algorithms because they can lead to either high false positive rates or low sensitivity. Algorithms that use the entire spectrum have been the most successful in this area; notable examples are the NSCRAD algorithm developed at Pacific Northwest National Laboratory and recently the nonnegative matrix factorization approach developed at Lawrence Berkeley National Laboratory (LBNL). These approaches use either spectral regions of interest or spectral decomposition to detect threat isotopes in the background.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Advanced Reactor Safeguards & Security Program: Cybersecurity Scenarios
The use of digital control systems and automation in advanced nuclear power systems introduces different types of vulnerabilities compared to legacy (i.e. analog) control systems that cyber adversaries can exploit. These vulnerabilities pose a challenge to reactor operators and cyber operations staff due to the dynamic nature of the event in which a human response or a lack of response can potentially evolve into a worsening plant condition. Using the Department of Homeland Security Cyber and Infrastructure Security Agency’s (CISA) critical infrastructure exercise framework, this document presents several cyber security scenarios typical of digital control systems that could be used in advanced reactor designs. These scenarios can be used in tabletop exercises to evaluate cyber security posture or conduct training on different aspects of cyber security, including detection, threat hunting using indicators of compromise, evaluating incident response, risk mitigation, incident reporting, information sharing and recovery.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Simulation of an enhanced TCAS 2 system in operation
Described is a computer simulation of a Boeing 737 aircraft equipped with an enhanced Traffic and Collision Avoidance System (TCAS II). In particular, an algorithm is developed which permits the computer simulation of the tracking of a target airplane by a Boeing 373 which has a TCAS II array mounted on top of its fuselage. This algorithm has four main components: namely, the target path, the noise source, the alpha-beta filter, and threat detection. The implementation of each of these four components is described. Furthermore, the areas where the present algorithm needs to be improved are also mentioned.
Rojas, R. G.↗
Safety on Earth From MARSS
ENSCO, Inc., developed the Meteorological and Atmospheric Real-time Safety Support (MARSS) system for real-time assessment of meteorological data displays and toxic material spills. MARSS also provides mock scenarios to guide preparations for emergencies involving meteorological hazards and toxic substances. Developed under a Small Business Innovation Research (SBIR) contract with Kennedy Space Center, MARSS was designed to measure how safe NASA and Air Force range safety personnel are while performing weather sensitive operations around launch pads. The system augments a ground operations safety plan that limits certain work operations to very specific weather conditions. It also provides toxic hazard prediction models to assist safety managers in planning for and reacting to releases of hazardous materials. MARSS can be used in agricultural, industrial, and scientific applications that require weather forecasts and predictions of toxic smoke movement. MARSS is also designed to protect urban areas, seaports, rail facilities, and airports from airborne releases of hazardous chemical substances. The system can integrate with local facility protection units and provide instant threat detection and assessment data that is reportable for local and national distribution.
Source record↗
Predicting the Impacts of Intravehicular Displays on Driving Performance with Human Performance Modeling
A challenge facing the U.S. National Highway Traffic Safety Administration (NHTSA), as well as international safety experts, is the need to educate car drivers about the dangers associated with performing distraction tasks while driving. Researchers working for the U.S. Army Research Laboratory have developed a technique for predicting the increase in mental workload that results when distraction tasks are combined with driving. They implement this technique using human performance modeling. They have predicted workload associated with driving combined with cell phone use. In addition, they have predicted the workload associated with driving military vehicles combined with threat detection. Their technique can be used by safety personnel internationally to demonstrate the dangers of combining distracter tasks with driving and to mitigate the safety risks.
Mitchell, Diane Kuhl↗
Innovating Cross-domain Solutions to Detect Emerging Biological Threats
Poster for CBDS&T conference
Yang, Jihoon [Sandia National Laboratories (SNL-CA↗
Recovery strategies for microburst encounters using reactive and forward-look wind shear detection
The threat of convective microburst wind shear phenomena to aircraft is studied. An attempt was made to quantify the benefits of forward-look sensing and to develop and test a candidate set of strategies for recovery from inadvertent microburst encounters during the landing approach. A batch simulation of various wind shear encounters was carried out; the simulation consisted of a point-mass aircraft model, an analytical microburst, and a simple wind shear detection scheme. It was found that forward-look alerts given 10 sec prior to microburst entry permitted recoveries to be made with little altitude loss.
Hinton, David A.↗
Detecting Unclassified Electromagnetic Signals for Secure Wireless Communication Using Open Set Recognition
We developed multiple machine learning methods for the detection and classification of new wireless communication waveforms, which is critical for targeted attacks in wireless networks and electronic warfare. Our machine learning models are capable of dynamically detecting security threats in near real time through our advanced open set recognition (OSR) approach. This model has demonstrated significant improvements in the detection of unknown waveforms, thereby enhancing the security and reliability of mission critical communications. Our approach to detecting uncertain security threats is novel; we advanced OSR techniques by incorporating domain knowledge of wireless signals. Specifically, we combined time and frequency domain model features to enhance the model’s performance. Utilizing an OSR approach eliminates the need for training data to be distributed similarly to the deployment environment and removes the requirement for the training set to contains all possible threat classes. This is crucial because it is often infeasible to determine and characterize all potential security threats in advance. Our model were trained on simulated data, generated in partnership with the University at Albany, State of New York. The data set contained a diverse array of wireless signals, including those with additive white Gaussian noise and multipath signals, with and without line of sight. This comprehensive training set allowed us to optimize our models to detect unknown waveforms under various challenging scenarios, such as low signal-to-noise ratios. By training on various waveforms, varying signal-to-noise ratio, and different sample sizes under normal conditions, our models were fine tuned to perform effectively in challenging environments.
99 - GENERAL AND MISCELLANEOUS↗
System for detecting objects that represent a threat
A description is given of a panoramic receiver. It is used as a sensor containing several detectors that respond to various types of signal and sound. The system described can be used for detecting objects that represent a threat, by using a sensor and a system for discriminating characteristic signals.
Riedl, Guenther↗