Publications

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Performance measures commonly used in systems security engineering tend to be static, linear, and have limited utility in addressing challenges to security performance from increasingly complex risk environments, adversary innovation, and disruptive technologies. Leveraging key concepts from resilience science offers an opportunity to advance next-generation systems security engineering to better describe the complexities, dynamism, and non-linearity observed in security performance—particularly in response to these challenges. This article introduces a multilayer network model and modified Continuous Time Markov Chain model that explicitly captures interdependencies in systems security engineering. The results and insights from a multilayer network model of security for a hypothetical nuclear power plant introduce how network-based metrics can incorporate resilience concepts into performance metrics for next generation systems security engineering.

Resilience has been defined as a priority for the US critical infrastructure. This paper presents a process for incorporating resiliency-derived metrics into security system evaluations. To support this analysis, we used a multi-layer network model (MLN) reflecting the defined security system of a hypothetical nuclear power plant to define what metrics would be useful in understanding a system's ability to absorb perturbation (i.e., system resilience). We defined measures focusing on the system's criticality, rapidity, diversity, and confidence at each network layer, simulated adversary path, and the system as a basis for understanding the system's resilience. For this hypothetical system, our metrics indicated the importance of physical infrastructure to overall system criticality, the relative confidence of physical sensors, and the lack of diversity in assessment activities (i.e., dependence on human evaluations). Refined model design and data outputs will enable more nuanced evaluations into temporal, geospatial, and human behavior considerations. Future studies can also extend these methodologies to capture respond and recover aspects of resilience, further supporting the protection of critical infrastructure.

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.

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The objective of this project was to increase the rate at which video data is processed using temporal frequency analysis. A common solution to increasing the speed of data processing is to increase the computing power of the system however size, weight and power (SWAP) constraints require computing power to be limited. This project focused on increasing the processing speed by reducing the expense of computing the Fourier Transform (FT).

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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.

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