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Air Force Institute of Technology

Theses/Dissertations

2022

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Full-Text Articles in Physical Sciences and Mathematics

Evaluating Deep Learning Explanations On Risc-V Assembly As A Reverse Engineering Aid, Daniel F. Koranek Dec 2022

Evaluating Deep Learning Explanations On Risc-V Assembly As A Reverse Engineering Aid, Daniel F. Koranek

Theses and Dissertations

This dissertation addresses several problems surrounding the detection of malware using deep learning models trained on assembly language examples. First, it examines the feasibility of detecting examples of malice using deep learning models trained on RISC-V instruction traces. Next, it examines whether models for detecting trace features and code features in RISC-V assembly can be made explainable (providing rationale for a model’s decision based upon the model’s internal workings) or interpretable (providing additional rationale as model output to support a human’s agreement with the model output). Third, this work examines ways in which it is possible to give additional contextual …

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Fitting Solar Panel Brdf Parameters To Out-Of-Plane Empirical Data, Michael R. Gross Dec 2022

Fitting Solar Panel Brdf Parameters To Out-Of-Plane Empirical Data, Michael R. Gross

Theses and Dissertations

The bidirectional reflectance distribution function (BRDF) describes material reflectance by describing how incident irradiance reflects into all possible scatter angles as a function of incident angle. However, a solar panel has unique features that are not featured in any of these previously known models. A previous project at the Air Force Institute of Technology (AFIT) created a novel microfacet-like BRDF to model a solar panel with a prominent diffractive feature present which had not been previously modeled. This BRDF was coded into MATLAB for modeling purposes and C++ to test its speed with a MEX function call. A previous thesis …

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Analytic Case Study Using Unsupervised Event Detection In Multivariate Time Series Data, Jeremy M. Wightman Sep 2022

Analytic Case Study Using Unsupervised Event Detection In Multivariate Time Series Data, Jeremy M. Wightman

Theses and Dissertations

Analysis of cyber-physical systems (CPS) has emerged as a critical domain for providing US Air Force and Space Force leadership decision advantage in air, space, and cyberspace. Legacy methods have been outpaced by evolving battlespaces and global peer-level challengers. Automation provides one way to decrease the time that analysis currently takes. This thesis presents an event detection automation system (EDAS) which utilizes deep learning models, distance metrics, and static thresholding to detect events. The EDAS automation is evaluated with case study of CPS domain experts in two parts. Part 1 uses the current methods for CPS analysis with a qualitative …

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Analyzing Microarchitectural Residue In Various Privilege Strata To Identify Computing Tasks, Tor J. Langehaug Sep 2022

Analyzing Microarchitectural Residue In Various Privilege Strata To Identify Computing Tasks, Tor J. Langehaug

Theses and Dissertations

Modern multi-tasking computer systems run numerous applications simultaneously. These applications must share hardware resources including the Central Processing Unit (CPU) and memory while maximizing each application’s performance. Tasks executing in this shared environment leave residue which should not reveal information. This dissertation applies machine learning and statistical analysis to evaluate task residue as footprints which can be correlated to identify tasks. The concept of privilege strata, drawn from an analogy with physical geology, organizes the investigation into the User, Operating System, and Hardware privilege strata. In the User Stratum, an adversary perspective is taken to build an interrogator program that …

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Orthogonal Arrays And Legendre Pairs, Kristopher N. Kilpatrick Sep 2022

Orthogonal Arrays And Legendre Pairs, Kristopher N. Kilpatrick

Theses and Dissertations

Well-designed experiments greatly improve test and evaluation. Efficient experiments reduce the cost and time of running tests while improving the quality of the information obtained. Orthogonal Arrays (OAs) and Hadamard matrices are used as designed experiments to glean as much information as possible about a process with limited resources. However, constructing OAs and Hadamard matrices in general is a very difficult problem. Finding Legendre pairs (LPs) results in the construction of Hadamard matrices. This research studies the classification problem of OAs and the existence problem of LPs. In doing so, it makes two contributions to the discipline. First, it improves …

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Modern Approaches And Theoretical Extensions To The Multivariate Kolmogorov Smirnov Test, Gonzalo Hernando Sep 2022

Modern Approaches And Theoretical Extensions To The Multivariate Kolmogorov Smirnov Test, Gonzalo Hernando

Theses and Dissertations

Most statistical tests are fully developed for univariate data, but when inference is required for multivariate data, univariate tests risk information loss and interpretability. This research 1) derives and extends the multivariate Komolgorov Smirnov test for 2 and into m-dimensions, 2) derives small sample critical values for the KS test that are not reliant on sample size simulations or correlation between variables, 3) extends large sample estimations and current KS implementations, and 4) provides sample size and power calculations in order to enable experimental design with respect to testing for differences in distributions. Through extensive simulation, we demonstrate that our …

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Quantum Error Detection Without Using Ancilla Qubits, Nicolas Guerrero Sep 2022

Quantum Error Detection Without Using Ancilla Qubits, Nicolas Guerrero

Theses and Dissertations

Quantum computers are beset by errors from a variety of sources. Although quantum error correction and detection codes have been developed since the 1990s, these codes require mid-circuit measurements in order to operate. In order to avoid these measurements we have developed a new error detection code that only requires state collapses at the end of the circuit, which we call no ancilla error detection (NAED). We investigate some of the mathematics behind NAED such as which codes can detect which errors. We then run NAED on three separate types of circuits: Greenberger–Horne–Zeilinger circuits, phase dependent circuits, and a quantum …

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Efficiency Quantification For Pulsed-Source Digital Holographic Wavefront Sensing, Steven A. Owens Sep 2022

Efficiency Quantification For Pulsed-Source Digital Holographic Wavefront Sensing, Steven A. Owens

Theses and Dissertations

The efficiencies of a digital holography (DH) system in the pulsed configuration and the off-axis image plane recording geometry are analyzed. First, the system efficiencies of an infrared-wavelength DH system in a homodyne-pulsed configuration are measured and compared to those of a visible-wavelength DH system in a homodyne-continuous-wave (CW) configuration. The total-system, excess-reference-noise, shot-noise-limit, and mixing efficiencies of the pulsed-source system were found to be consistent with those of the CW-source system. This indicated no new efficiencies were necessary to characterize pulsed-source systems when no temporal delay exists between the pulses. The consistency of efficiencies also showed infrared DH systems …

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Analysis Of Graph Layout Algorithms For Use In Command And Control Network Graphs, Matthew R. Stone Sep 2022

Analysis Of Graph Layout Algorithms For Use In Command And Control Network Graphs, Matthew R. Stone

Theses and Dissertations

This research is intended to determine which styles of layout algorithm are well suited to Command and Control (C2) network graphs to replace current manual layout methods. Manual methods are time intensive and an automated layout algorithm should decrease the time spent creating network graphs. Simulations on realistic synthetically generated graphs provide information to help infer which algorithms perform better than others on this problem. Data is generated using statistics drawn from multiple real world C2 network graphs. The three algorithms tested against this data are the Spectral algorithm, the Dot algorithm, and the Fruchterman-Reingold algorithm. The results include a …

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Optimizing Optical Switching Of Non-Linear Optimizing Optical Switching Of Non-Linear Hyperbolic Metamaterials, James A. Ethridge Sep 2022

Optimizing Optical Switching Of Non-Linear Optimizing Optical Switching Of Non-Linear Hyperbolic Metamaterials, James A. Ethridge

Theses and Dissertations

Modern optical materials are engineered to be used as optical devices in specific applications, such as optical computing. For optical computing, efficient forms of a particular device, the optical switch, still have not been successfully demonstrated. This problem is addressed in this research through the use of designed optical metamaterials, specifically, hyperbolic metamaterials, which offer the possibility of large non-linear properties with a low switching intensity. One-dimensional layered hyperbolic metamaterials composed of alternating layers of metal and dielectric were used here, with ITO as the metal and SiO2 as the dielectric. The non-linear behavior of the ITO/SiO2 layered …

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Leveraging Subject Matter Expertise To Optimize Machine Learning Techniques For Air And Space Applications, Philip Y. Cho Sep 2022

Leveraging Subject Matter Expertise To Optimize Machine Learning Techniques For Air And Space Applications, Philip Y. Cho

Theses and Dissertations

We develop new machine learning and statistical methods that are tailored for Air and Space applications through the incorporation of subject matter expertise. In particular, we focus on three separate research thrusts that each represents a different type of subject matter knowledge, modeling approach, and application. In our first thrust, we incorporate knowledge of natural phenomena to design a neural network algorithm for localizing point defects in transmission electron microscopy (TEM) images of crystalline materials. In our second research thrust, we use Bayesian feature selection and regression to analyze the relationship between fighter pilot attributes and flight mishap rates. We …

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Development Of A Security-Focused Multi-Channel Communication Protocol And Associated Quality Of Secure Service (Qoss) Metrics, Paul M. Simon Sep 2022

Development Of A Security-Focused Multi-Channel Communication Protocol And Associated Quality Of Secure Service (Qoss) Metrics, Paul M. Simon

Theses and Dissertations

The threat of eavesdropping, and the challenge of recognizing and correcting for corrupted or suppressed information in communication systems is a consistent challenge. Effectively managing protection mechanisms requires an ability to accurately gauge the likelihood or severity of a threat, and adapt the security features available in a system to mitigate the threat. This research focuses on the design and development of a security-focused communication protocol at the session-layer based on a re-prioritized communication architecture model and associated metrics. From a probabilistic model that considers data leakage and data corruption as surrogates for breaches of confidentiality and integrity, a set …

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Learning Robust Radio Frequency Fingerprints Using Deep Convolutional Neural Networks, Jose A. Gutierrez Del Arroyo Sep 2022

Learning Robust Radio Frequency Fingerprints Using Deep Convolutional Neural Networks, Jose A. Gutierrez Del Arroyo

Theses and Dissertations

Radio Frequency Fingerprinting (RFF) techniques, which attribute uniquely identifiable signal distortions to emitters via Machine Learning (ML) classifiers, are limited by fingerprint variability under different operational conditions. First, this work studied the effect of frequency channel for typical RFF techniques. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models leads to deterioration in MCC to under 0.05 (random guess), indicating that single-channel models are inadequate for realistic operation. Second, this work presented a novel way of studying fingerprint variability through Fingerprint Extraction through Distortion Reconstruction (FEDR), a …

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Statistical Inference On Desirability Function Optimal Points To Evaluate Multi-Objective Response Surfaces, Peter A. Calhoun Sep 2022

Statistical Inference On Desirability Function Optimal Points To Evaluate Multi-Objective Response Surfaces, Peter A. Calhoun

Theses and Dissertations

A shortfall of the Derringer and Suich (1980) desirability function is lack of inferential methods to quantify uncertainty. Most articles for addressing uncertainty usually involve robust methods, providing a point estimate that is less affected by variation. Few articles address confidence intervals or bands but not specifically for the Derringer and Suich method. This research provides two valuable contributions to the field of response surface methodology. The first contribution is evaluating the effect of correlation and plane angles on Derringer and Suich optimal solutions. The second contribution proposes and compares 8 inferential methods--both univariate and multivariate--for creating confidence intervals on …

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Enabling Rapid Chemical Analysis Of Plutonium Alloys Via Machine Learning-Enhanced Atomic Spectroscopy Techniques, Ashwin P. Rao Sep 2022

Enabling Rapid Chemical Analysis Of Plutonium Alloys Via Machine Learning-Enhanced Atomic Spectroscopy Techniques, Ashwin P. Rao

Theses and Dissertations

Analytical atomic spectroscopy methods have the potential to provide solutions for rapid, high fidelity chemical analysis of plutonium alloys. Implementing these methods with advanced analytical techniques can help reduce the chemical analysis time needed for plutonium pit production, directly enabling the 80 pit-per-year by 2030 manufacturing goal outlined in the 2018 Nuclear Posture Review. Two commercial, handheld elemental analyzers were validated for potential in situ analysis of Pu. A handheld XRF device was able to detect gallium in a Pu surrogate matrix with a detection limit of 0.002 wt% and a mean error of 8%. A handheld LIBS device was …

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Improving Country Conflict And Peace Modeling: Datasets, Imputations, And Hierarchical Clustering, Benjamin D. Leiby Sep 2022

Improving Country Conflict And Peace Modeling: Datasets, Imputations, And Hierarchical Clustering, Benjamin D. Leiby

Theses and Dissertations

Many disparate datasets exist that provide country attributes covering political, economic, and social aspects. Unfortunately, this data often does not include all countries nor is the data complete for those countries included, as measured by the dataset’s missingness. This research addresses these dataset shortfalls in predicting country instability by considering country attributes in all aspects as well as in greater thresholds of missingness. First, a structured summary of past research is presented framed by a developed casual taxonomy and functional ontology. Additionally, a novel imputation technique for very large datasets is presented to account for moderate missingness in the expanded …

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Generative Methods, Meta-Learning, And Meta-Heuristics For Robust Cyber Defense, Marc W. Chale Sep 2022

Generative Methods, Meta-Learning, And Meta-Heuristics For Robust Cyber Defense, Marc W. Chale

Theses and Dissertations

Cyberspace is the digital communications network that supports the internet of battlefield things (IoBT), the model by which defense-centric sensors, computers, actuators and humans are digitally connected. A secure IoBT infrastructure facilitates real time implementation of the observe, orient, decide, act (OODA) loop across distributed subsystems. Successful hacking efforts by cyber criminals and strategic adversaries suggest that cyber systems such as the IoBT are not secure. Three lines of effort demonstrate a path towards a more robust IoBT. First, a baseline data set of enterprise cyber network traffic was collected and modelled with generative methods allowing the generation of realistic, …

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Full Pattern Analysis And Comparison Of The Center Fed And Offset Fed Cassegrain Antennas With Large Focal Length To Diameter Ratios For High Power Microwave Transmission, Derek W. Mantzke Jun 2022

Full Pattern Analysis And Comparison Of The Center Fed And Offset Fed Cassegrain Antennas With Large Focal Length To Diameter Ratios For High Power Microwave Transmission, Derek W. Mantzke

Theses and Dissertations

High power microwaves (HPM) have been a topic of research since the Cold War era. This paper will present a comparison between two Cassegrain-type antennas: the axially, or center fed, and the offset fed. Specifically, the 10 GHz operating frequency will be investigated with large focal length to diameter () ratios. Beam patterns which encompass the entire radiation pattern will be included for data validation and optimization. The simulations will follow a design of experiments factorial model to ensure all possible combinations of prescribed parameters are included, including an analysis of variance (ANOVA) study to find parameter influence on the …

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Scheduling For Space Tracking And Heterogeneous Sensor Environments, Gabriel H. Greve Jun 2022

Scheduling For Space Tracking And Heterogeneous Sensor Environments, Gabriel H. Greve

Theses and Dissertations

This dissertation draws on the fields of heuristic and meta-heuristic algorithm development, resource allocation problems, and scheduling to address key Air Force problems. The world runs on many schedules. People depend upon them and expect these schedules to be accurate. A process is needed where schedules can be dynamically adjusted to allow tasks to be completed efficiently. For example, the Space Surveillance Network relies on a schedule to track objects in space. The schedule must use sensor resources to track as many high-priority satellites as possible to obtain orbit paths and to warn of collision paths. Any collisions that occurred …

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Innovative Heuristics To Improve The Latent Dirichlet Allocation Methodology For Textual Analysis And A New Modernized Topic Modeling Approach, Jamie T. Zimmerman Jun 2022

Innovative Heuristics To Improve The Latent Dirichlet Allocation Methodology For Textual Analysis And A New Modernized Topic Modeling Approach, Jamie T. Zimmerman

Theses and Dissertations

Natural Language Processing is a complex method of data mining the vast trove of documents created and made available every day. Topic modeling seeks to identify the topics within textual corpora with limited human input into the process to speed analysis. Current topic modeling techniques used in Natural Language Processing have limitations in the pre-processing steps. This dissertation studies topic modeling techniques, those limitations in the pre-processing, and introduces new algorithms to gain improvements from existing topic modeling techniques while being competitive with computational complexity. This research introduces four contributions to the field of Natural Language Processing and topic modeling. …

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Methods For Focal Plane Array Resolution Estimation Using Random Laser Speckle In Non-Paraxial Geometries, Phillip J. Plummer Jun 2022

Methods For Focal Plane Array Resolution Estimation Using Random Laser Speckle In Non-Paraxial Geometries, Phillip J. Plummer

Theses and Dissertations

The infrared (IR) imaging community has a need for direct IR detector evaluation due to the continued demand for small pixel pitch detectors, the emergence of strained-layer-super-lattice devices, and the associated lateral carrier diffusion issues. Conventional laser speckle-based modulation transfer function (MTF) estimation is dependent on Fresnel propagation and a wide-sense-stationary input random process, limiting the use of this approach for lambda (wavelength)-scale IR devices. This dissertation develops two alternative methodologies for speckle-based resolution evaluation of IR focal plane arrays (FPAs). Both techniques are formulated using Rayleigh-Sommerfield electric field propagation, making them valid in the non-paraxial geometries dictated for resolution …

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Evaluating A Statistical-Based Assessment Tool For Stratifying Risk Among U.S. Air Force Organizations, Tiffany A. Low Jun 2022

Evaluating A Statistical-Based Assessment Tool For Stratifying Risk Among U.S. Air Force Organizations, Tiffany A. Low

Theses and Dissertations

The Air Force Inspection System is a proponent of utilizing a risk-based sampling strategy (RBSS) for conducting inspections from major command levels down to the unit level. The strategy identifies areas deemed most important or risky by commanders and prioritizes them accordingly for an independent assessment by the Inspector General. While Air Force regulation specifies the need to use a RBSS for inspection, the implementation process is delegated to individual commands and, subsequently, wings. The 23rd Wing, the sponsor for this research, directed us to analyze a RBSS tool highlighted as an example from which to adopt for those units …

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Approximate Dynamic Programming For An Unmanned Aerial Vehicle Routing Problem With Obstacles And Stochastic Target Arrivals, Kassie M. Gurnell Mar 2022

Approximate Dynamic Programming For An Unmanned Aerial Vehicle Routing Problem With Obstacles And Stochastic Target Arrivals, Kassie M. Gurnell

Theses and Dissertations

The United States Air Force is investing in artificial intelligence (AI) to speed analysis in efforts to modernize the use of autonomous unmanned combat aerial vehicles (AUCAVs) in strike coordination and reconnaissance (SCAR) missions. This research examines an AUCAVs ability to execute target strikes and provide reconnaissance in a SCAR mission. An orienteering problem is formulated as anMarkov decision process (MDP) model wherein a single AUCAV must optimize its target route to aid in eliminating time-sensitive targets and collect imagery of requested named areas of interest while evading surface-to-air missile (SAM) battery threats imposed as obstacles. The AUCAV adjusts its …

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An Entity-Component System Based, Ieee Dis Interoperability Interface, Noah W. Scott Mar 2022

An Entity-Component System Based, Ieee Dis Interoperability Interface, Noah W. Scott

Theses and Dissertations

In practice, there are several different methods of organizing data within a given software to fulfil its function. The method known as the Entity-Component System (ECS) is a software architecture where data components define entities. These components are stored as organized lists which are operated upon by systems to inject the system's desired behavior. Data is sent across the networks to communicate between simulation nodes as Protocol Data Units (PDUs). When sending PDUs across a network protocol, each simulation represents a common understanding of the world at the desired level of detail. DIS-compliant simulations are commonly written using an Object-Oriented …

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Bayesian Convolutional Neural Network With Prediction Smoothing And Adversarial Class Thresholds, Noah M. Miller Mar 2022

Bayesian Convolutional Neural Network With Prediction Smoothing And Adversarial Class Thresholds, Noah M. Miller

Theses and Dissertations

Using convolutional neural networks (CNNs) for image classification for each frame in a video is a very common technique. Unfortunately, CNNs are very brittle and have a tendency to be over confident in their predictions. This can lead to what we will refer to as “flickering,” which is when the predictions between frames jump back and forth between classes. In this paper, new methods are proposed to combat these shortcomings. This paper utilizes a Bayesian CNN which allows for a distribution of outputs on each data point instead of just a point estimate. These distributions are then smoothed over multiple …

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Using Generative Adversarial Networks To Augment Unmanned Aerial Vehicle Image Classification Training Sets, Benjamin J. Mccloskey Mar 2022

Using Generative Adversarial Networks To Augment Unmanned Aerial Vehicle Image Classification Training Sets, Benjamin J. Mccloskey

Theses and Dissertations

A challenging task in computer vision is finding techniques to improve the object detection and classification capabilities of ML models used for processing images acquired by moving aerial platforms. This research explores if GAN augmented UAV training sets can increase the generalizability of a detection model trained on said data. To answer this question, the YOLOv4-Tiny Object Detection Model was trained with aerial image training sets depicting rural environments. The salient objects within the frames were recreated using various GAN architectures, placed back into the original frames, and the augmented frames appended to the original training sets. GAN augmentation on …

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Team Air Combat Using Model-Based Reinforcement Learning, David A. Mottice Mar 2022

Team Air Combat Using Model-Based Reinforcement Learning, David A. Mottice

Theses and Dissertations

We formulate the first generalized air combat maneuvering problem (ACMP), called the MvN ACMP, wherein M friendly AUCAVs engage against N enemy AUCAVs, developing a Markov decision process (MDP) model to control the team of M Blue AUCAVs. The MDP model leverages a 5-degree-of-freedom aircraft state transition model and formulates a directed energy weapon capability. Instead, a model-based reinforcement learning approach is adopted wherein an approximate policy iteration algorithmic strategy is implemented to attain high-quality approximate policies relative to a high performing benchmark policy. The ADP algorithm utilizes a multi-layer neural network for the value function approximation regression mechanism. One-versus-one …

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Classification And Keyword Identification Of Covid 19 Misinformation On Social Media: A Framework For Semantic Analysis, Grace Y. Smith Mar 2022

Classification And Keyword Identification Of Covid 19 Misinformation On Social Media: A Framework For Semantic Analysis, Grace Y. Smith

Theses and Dissertations

The growing surge of misinformation among COVID-19 communication can pose great hindrance to truth, magnify distrust in policy makers and/or degrade authorities’ credibility, and it can even harm public health. Classification of textual context on social media data relating to COVID-19 is an effective tool to combat misinformation on social media platforms. In this research, Twitter data was leveraged to 1) develop classification methods to detect misinformation and identify Tweet sentiment with respect to COVID-19 and 2) develop a human-in-the-loop interactive framework to enable identification of keywords associated with social context, here, being misinformation regarding COVID-19. 1) Six fusion-based classification …

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Predicting Tf33-Pw-100a Engine Failures Due To Oil Issues Using Survival Analyses, Anna M. Davis Mar 2022

Predicting Tf33-Pw-100a Engine Failures Due To Oil Issues Using Survival Analyses, Anna M. Davis

Theses and Dissertations

In 2007, the Office of the Assistant Secretary of Defense for Sustainment pushed for the need to transition to a Condition Based Maintenance Plus (CBM ) initiative for weapon systems in the U.S. Department of Defense. The CBM initiative can help increase aircraft availability (AA) for the United States Air Force. There are many reasons where AA can be affected but one such issue is engine availability primarily due to oil issues. Within the CBM perspective, this study examines the risk of a jet engine failure due to an oil issue and attempts to predict an engines time until next …

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Hydrologic Profiles And Geospatial Trend Analysis Evaluating Recurrent Flooding At Coastal U.S. Air Force Installations, Dylan D. Bechen Mar 2022

Hydrologic Profiles And Geospatial Trend Analysis Evaluating Recurrent Flooding At Coastal U.S. Air Force Installations, Dylan D. Bechen

Theses and Dissertations

Military installations are exposed to numerous threats, including a changing climate and the risk of recurrent flooding. The four components of recurrent flooding are sea-level rise, tidal fluctuations, storm surges, and precipitation. This research analyzed 40 years of historical precipitation and tidal data at 17 coastal U.S. Air Force installations using indicators of both peak and threshold exceedances to identify long-term temporal trends in the hydrologic components that make up recurrent flood risk, establishing an installation’s “hydrologic profile” which can be used to better inform decision makers when evaluating portfolio-wide adaptation strategies and prioritization of long-term infrastructure investments.

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