Publications

Results 1–25 of 75
Skip to search filters

Revisiting Current Paradigms: Subject Matter Expert Views on High Consequence Facility Security Assessments

Journal of Nuclear Materials Management

Gunda, Thushara G.; Caskey, Susan A.; Williams, Adam D.; Birch, Gabriel C.

Security assessments support decision-makers' ability to evaluate current capabilities of high consequence facilities (HCF) to respond to possible attacks. However, increasing complexity of today's operational environment requires a critical review of traditional approaches to ensure that implemented assessments are providing relevant and timely insights into security of HCFs. Using interviews and focus groups with diverse subject matter experts (SMEs), this study evaluated the current state of security assessments and identified opportunities to achieve a more "ideal" state. The SME-based data underscored the value of a systems approach for understanding the impacts of changing operational designs and contexts (as well as cultural influences) on security to address methodological shortcomings of traditional assessment processes. These findings can be used to inform the development of new approaches to HCF security assessments that are able to more accurately reflect changing operational environments and effectively mitigate concerns arising from new adversary capabilities.

More Details
TYPE Journal Article YEAR 2022
OSTI

A Complex Systems Approach to Develop a Multilayer Network Model for High Consequence Facility Security

Springer Proceedings in Complexity

Williams, Adam D.; Birch, Gabriel C.; Caskey, Susan A.; Gunda, Thushara G.; Wingo, Jamie; Adams, Thomas

Protecting high consequence facilities (HCF) from malicious attacks is challenged by today’s increasingly complex, multi-faceted, and interdependent operational environments and threat domains. Building on current approaches, insights from complex systems and network science can better incorporate multidomain interactions observed in HCF security operations. These observations and qualitative HCF security expert data support invoking a multilayer modeling approach for HCF security to shift from a “reactive” to a “proactive” paradigm that better explores HCF security dynamics and resilience not captured in traditional approaches. After exploring these multi-domain interactions, this paper introduces how systems theory and network science insights can be leveraged to describe HCF security as complex, interdependent multilayer directed networks. A hypothetical example then demonstrates the utility of such an approach, followed by a discussion on key insights and implications of incorporating multilayer network analytical performance measures into HCF security.

More Details
TYPE Conference Poster YEAR 2021
ScopusOSTI

A multiplex complex systems model for engineering security systems

Systems Security Symposium, SSS 2020 - Conference Proceedings

Williams, Adam D.; Birch, Gabriel C.

Existing security models are highly linear and fail to capture the rich interactions that occur across security technology, infrastructure, cybersecurity, and human/organizational components. In this work, we will leverage insights from resilience science, complex system theory, and network theory to develop a next-generation security model based on these interactions to address challenges in complex, nonlinear risk environments and against innovative and disruptive technologies. Developing such a model is a key step forward toward a dynamic security paradigm (e.g., shifting from detection to anticipation) and establishing the foundation for designing next-generation physical security systems against evolving threats in uncontrolled or contested operational environments.

More Details
TYPE Conference Poster YEAR 2020
ScopusOSTI

Performance evaluation of two optical architectures for task-specific compressive classification

Optical Engineering

Redman, Brian J.; Dagel, Amber L.; Galiardi, Meghan A.; LaCasse, Charles F.; Quach, Tu-Thach Q.; Birch, Gabriel C.

Many optical systems are used for specific tasks such as classification. Of these systems, the majority are designed to maximize image quality for human observers. However, machine learning classification algorithms do not require the same data representation used by humans. We investigate the compressive optical systems optimized for a specific machine sensing task. Two compressive optical architectures are examined: an array of prisms and neutral density filters where each prism and neutral density filter pair realizes one datum from an optimized compressive sensing matrix, and another architecture using conventional optics to image the aperture onto the detector, a prism array to divide the aperture, and a pixelated attenuation mask in the intermediate image plane. We discuss the design, simulation, and trade-offs of these systems built for compressed classification of the Modified National Institute of Standards and Technology dataset. Both architectures achieve classification accuracies within 3% of the optimized sensing matrix for compression ranging from 98.85% to 99.87%. The performance of the systems with 98.85% compression were between an F / 2 and F / 4 imaging system in the presence of noise.

More Details
TYPE Journal Article YEAR 2020
ScopusOSTIDOI

Temporal Frequency Analysis: Target Isolation and Signal Optimization

Conference Record - Asilomar Conference on Signals, Systems and Computers

Stubbs, Jaclynn J.; Birch, Gabriel C.; Woo, Bryana L.; Kouhestani, Camron G.; Novick, David K.

Unmanned aircraft systems (UASs) have grown significantly within the private sector with ease of acquisition and platform capabilities far outstretching what previously existed. Where once the operation of these platforms was limited to skilled individuals, increased computational power, manufacturing techniques, and increased autonomy allows inexperienced individuals to skillfully maneuver these devices. With this rise in consumer use of UAS comes an increased security concern regarding their use for malicious intent.The focus area of counter UAS (CUAS) remains a challenging space due to a small cross-sectioned UAS's ability to move in all three dimensions, attain very high speeds, carry payloads of notable weight, and avoid standard delay techniques.We examine frequency analysis of pixel fluctuation over time to exploit the temporal frequency signature present in UAS imagery. This signature allows for lower pixels-on-target detection [1]. The methodology also acts as a method of assessment due to the distinct frequency signatures of UAS when examined against the standard nuisance alarms such as birds. The temporal frequency analysis (TFA) method demonstrates a UAS detection and assessment method. In this paper we discuss signal processing and Fourier filter optimization methodologies that increase UAS contrast.

More Details
TYPE Conference Poster YEAR 2019
ScopusOSTIDOI

Optimizing a Compressive Imager for Machine Learning Tasks

Conference Record - Asilomar Conference on Signals, Systems and Computers

Redman, Brian J.; Calzada, Daniel; Wingo, Jamie; Quach, Tu-Thach Q.; Galiardi, Meghan; Dagel, Amber L.; LaCasse, Charles F.; Birch, Gabriel C.

Images are often not the optimal data form to perform machine learning tasks such as scene classification. Compressive classification can reduce the size, weight, and power of a system by selecting the minimum information while maximizing classification accuracy.In this work we present designs and simulations of prism arrays which realize sensing matrices using a monolithic element. The sensing matrix is optimized using a neural network architecture to maximize classification accuracy of the MNIST dataset while considering the blurring caused by the size of each prism. Simulated optical hardware performance for a range of prism sizes are reported.

More Details
TYPE Conference Poster YEAR 2019
ScopusOSTIDOI
Results 1–25 of 75
Results 1–25 of 75