Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Machine learning (10)
- Neural networks (4)
- Deep learning (3)
- Quantum computing (3)
- Aerial refueling (2)
-
- Anomaly detection (2)
- Bluetooth (2)
- Communication model (2)
- Computer vision (2)
- Confidentiality (2)
- Cybersecurity (2)
- False injection (2)
- Missile defense (2)
- TCAS (2)
- Threat map (2)
- 4G wireless networks (1)
- 802.11ac (Standard) (1)
- AFSIM (1)
- Adaptable response (1)
- Aircraft inspections (1)
- Anti-Malware (1)
- Artificial intelligence (1)
- Atmospheric regimes (1)
- Automation (1)
- Autonomous satellite servicing (1)
- BLE (1)
- Beam tracking (1)
- Beamforming (1)
- Binary diversity (1)
- Capturability (1)
- Publication Type
Articles 1 - 30 of 52
Full-Text Articles in Physical Sciences and Mathematics
Extending The Quality Of Secure Service Model To Multi-Hop Networks, Paul M. Simon, Scott R. Graham
Extending The Quality Of Secure Service Model To Multi-Hop Networks, Paul M. Simon, Scott R. Graham
Faculty Publications
Rarely are communications networks point-to-point. In most cases, transceiver relay stations exist between transmitter and receiver end-points. These relay stations, while essential for controlling cost and adding flexibility to network architectures, reduce the overall security of the respective network. In an effort to quantify that reduction, we extend the Quality of Secure Service (QoSS) model to these complex networks, specifically multi-hop networks. In this approach, the quantification of security is based upon probabilities that adversarial listeners and disruptors gain access to or manipulate transmitted data on one or more of these multi-hop channels. Message fragmentation and duplication across available channels …
Traffic Collision Avoidance System: False Injection Viability, John Hannah, Robert F. Mills, Richard A. Dill, Douglas D. Hodson
Traffic Collision Avoidance System: False Injection Viability, John Hannah, Robert F. Mills, Richard A. Dill, Douglas D. Hodson
Faculty Publications
Safety is a simple concept but an abstract task, specifically with aircraft. One critical safety system, the Traffic Collision Avoidance System II (TCAS), protects against mid-air collisions by predicting the course of other aircraft, determining the possibility of collision, and issuing a resolution advisory for avoidance. Previous research to identify vulnerabilities associated with TCAS’s communication processes discovered that a false injection attack presents the most comprehensive risk to veritable trust in TCAS, allowing for a mid-air collision. This research explores the viability of successfully executing a false injection attack against a target aircraft, triggering a resolution advisory. Monetary constraints precluded …
Cognition-Enhanced Machine Learning For Better Predictions With Limited Data, Florian Sense, Ryan Wood, Michael G. Collins, Joshua Fiechter, Aihua W. Wood, Michael Krusmark, Tiffany Jastrzembski, Christopher W. Myers
Cognition-Enhanced Machine Learning For Better Predictions With Limited Data, Florian Sense, Ryan Wood, Michael G. Collins, Joshua Fiechter, Aihua W. Wood, Michael Krusmark, Tiffany Jastrzembski, Christopher W. Myers
Faculty Publications
The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can …
Advancing Proper Dataset Partitioning And Classification Of Visual Search And The Vigilance Decrement Using Eeg Deep Learning Algorithms, Alexander J. Kamrud
Advancing Proper Dataset Partitioning And Classification Of Visual Search And The Vigilance Decrement Using Eeg Deep Learning Algorithms, Alexander J. Kamrud
Theses and Dissertations
Electroencephalography (EEG) classification of visual search and vigilance tasks has vast potential in its benefits. In future human-machine teaming systems, EEG could act as the tool for operator state assessment, enabling AI teammates to know when to assist the operator in these tasks, with the potential to lead to increased safety of operations, better training systems for our operators, and improved operational effectiveness. This research investigates deep learning methods which utilize EEG signals to classify the efficiency of an operator's search and to classify whether an operator is in a decrement during a vigilance type task, and investigates performing these …
Determining Physical Characteristics Through Information Leakage In 802.11ac Beamforming, Albert D. Taglieri
Determining Physical Characteristics Through Information Leakage In 802.11ac Beamforming, Albert D. Taglieri
Theses and Dissertations
The risk of information leakage in 802.11ac allows an eavesdropper to monitor wireless traffic and correlate physical locations between devices, as well as environment changes such as the motion of a person. Previous pattern-analysis mitigation methods, which used nonexistent devices to fool an eavesdropper, are not effective in an 802.11ac network, because devices on the network can be correlated to their physical location, which a nonexistent device does not have. Further, additional information about motion in the target environment can be observed and analyzed, providing a new potential for pattern analysis and sensing. 802.11ac makes it possible to plug in …
Enterprise Resource Allocation For Intruder Detection And Interception, Adam B. Haywood
Enterprise Resource Allocation For Intruder Detection And Interception, Adam B. Haywood
Theses and Dissertations
This research considers the problem of an intruder attempting to traverse a defender's territory in which the defender locates and employs disparate sets of resources to lower the probability of a successful intrusion. The research is conducted in the form of three related research components. The first component examines the problem in which the defender subdivides their territory into spatial stages and knows the plan of intrusion. Alternative resource-probability modeling techniques as well as variable bounding techniques are examined to improve the convergence of global solvers for this nonlinear, nonconvex optimization problem. The second component studies a similar problem but …
Characterizing Convolutional Neural Network Early-Learning And Accelerating Non-Adaptive, First-Order Methods With Localized Lagrangian Restricted Memory Level Bundling, Benjamin O. Morris
Characterizing Convolutional Neural Network Early-Learning And Accelerating Non-Adaptive, First-Order Methods With Localized Lagrangian Restricted Memory Level Bundling, Benjamin O. Morris
Theses and Dissertations
This dissertation studies the underlying optimization problem encountered during the early-learning stages of convolutional neural networks and introduces a training algorithm competitive with existing state-of-the-art methods. First, a Design of Experiments method is introduced to systematically measure empirical second-order Lipschitz upper bound and region size estimates for local regions of convolutional neural network loss surfaces experienced during the early-learning stages. This method demonstrates that architecture choices can significantly impact the local loss surfaces traversed during training. Next, a Design of Experiments method is used to study the effects convolutional neural network architecture hyperparameters have on different optimization routines' abilities to …
Strengthening Criteria Independence Through Optimization Of Alternative Value Ratio Comparisons, Joseph P. Kristbaum, Frank W. Ciarallo
Strengthening Criteria Independence Through Optimization Of Alternative Value Ratio Comparisons, Joseph P. Kristbaum, Frank W. Ciarallo
Faculty Publications
Every decision maker’s internal scale is different based on a myriad of possible factors unique to that decision maker. Conflicting criteria within and between alternatives in multicriteria decision making can create negative effects within the weighting schemes and amplify preference biases and scale disparities between decision makers in a group decision context. Additionally, the weighting of group decision-making frameworks can intensify the already skewed criteria values. When making judgments against requirements, it may be preferable to reduce scale trend distortions between decision makers as much as possible. Previous research supports that certain information presentation modes can significantly reduce preference bias …
Rotating Scatter Mask For Directional Radiation Detection And Imaging, Darren Holland, Robert Olesen, Larry Burggraf, Buckley O'Day, James E. Bevins
Rotating Scatter Mask For Directional Radiation Detection And Imaging, Darren Holland, Robert Olesen, Larry Burggraf, Buckley O'Day, James E. Bevins
AFIT Patents
A radiation imaging system images a distributed source of radiation from an unknown direction by rotating a scatter mask around a central axis. The scatter mask has a pixelated outer surface of tangentially oriented, flat geometric surfaces that are spherically varying in radial dimension that corresponds to a discrete amount of attenuation. Rotation position of the scatter mask is tracked as a function of time. Radiation counts from gamma and/or neutron radiation are received from at least one radiation detector that is positioned at or near the central axis. A rotation-angle dependent detector response curve (DRC) is generated based on …
Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco [*], Torrey J. Wagner, Brent T. Langhals
Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco [*], Torrey J. Wagner, Brent T. Langhals
Faculty Publications
Random forest and neural network algorithms are applied to identify cloud cover using 10 of the wavelength bands available in Landsat 8 imagery. The methods classify each pixel into 4 different classes: clear, cloud shadow, light cloud, or cloud. The first method is based on a fully connected neural network with ten input neurons, two hidden layers of 8 and 10 neurons respectively, and a single-neuron output for each class. This type of model is considered with and without L2 regularization applied to the kernel weighting. The final model type is a random forest classifier created from an ensemble of …
Synthetic Aperture Radar Image Recognition Of Armored Vehicles, Christopher Szul [*], Torrey J. Wagner, Brent T. Langhals
Synthetic Aperture Radar Image Recognition Of Armored Vehicles, Christopher Szul [*], Torrey J. Wagner, Brent T. Langhals
Faculty Publications
Synthetic Aperture Radar (SAR) imagery is not affected by weather and allows for day-and-night observations, however it can be difficult to interpret. This work applies classical and neural network machine learning techniques to perform image classification of SAR imagery. The Moving and Stationary Target Acquisition and Recognition dataset from the Air Force Research Laboratory was used, which contained 2,987 total observations of the BMP-2, BTR-70, and T-72 vehicles. Using a 75%/25% train/test split, the classical model achieved an average multi-class image recognition accuracy of 70%, while a convolutional neural network was able to achieve a 97% accuracy with lower model …
A Quantitative Argument For Autonomous Aerial Defense Overembedded Missile Systems To Thwart Cruise Threats, Andrew R. Davis
A Quantitative Argument For Autonomous Aerial Defense Overembedded Missile Systems To Thwart Cruise Threats, Andrew R. Davis
Theses and Dissertations
Given the high cost of missile defense systems, their ability to be overwhelmed, and rising tensions between the U.S. and adversaries in the Indo-Pacific region, a new modeled is proposed to investigate a new approach to missile defense. The Autonomous Aerial Defense Against Missiles (AADAM) system leverages reusable, small-scale UAVs to propose a cheaper, more effective system in defending against cruise missile threats. The aim of this system is to provide and additional layer in current missile defense strategies at lower-cost. This modeled system is found to outperform a modeled Patriot system in close-range interception of designated assets, with no …
Evolutionary Generation Of Diversity In Embedded Binary Executables For Cyber Resiliency, Mitchell D. I. Hirschfeld
Evolutionary Generation Of Diversity In Embedded Binary Executables For Cyber Resiliency, Mitchell D. I. Hirschfeld
Theses and Dissertations
Hardening avionics systems against cyber attack is difficult and expensive. Attackers benefit from a "break one, break all" advantage due to the dominant mono-culture of automated systems. Also, undecidability of behavioral equivalence for arbitrary algorithms prevents the provable absence of undesired behaviors within the original specification. This research presents results of computational experiments using bio-inspired genetic programming to generate diverse implementations of executable software and thereby disrupt the mono-culture. Diversity is measured using the SSDeep context triggered piecewise hashing algorithm. Experiments are divided into two phases. Phase I explores the use of semantically-equivalent alterations that retain the specified behavior of …
Year-Independent Prediction Of Food Insecurity Using Classical & Neural Network Machine Learning Methods, Caleb Christiansen, Torrey J. Wagner, Brent Langhals
Year-Independent Prediction Of Food Insecurity Using Classical & Neural Network Machine Learning Methods, Caleb Christiansen, Torrey J. Wagner, Brent Langhals
Faculty Publications
Current food crisis predictions are developed by the Famine Early Warning System Network, but they fail to classify the majority of food crisis outbreaks with model metrics of recall (0.23), precision (0.42), and f1 (0.30). In this work, using a World Bank dataset, classical and neural network (NN) machine learning algorithms were developed to predict food crises in 21 countries. The best classical logistic regression algorithm achieved a high level of significance (p < 0.001) and precision (0.75) but was deficient in recall (0.20) and f1 (0.32). Of particular interest, the classical algorithm indicated that the vegetation index and the food price index were both positively correlated with food crises. A novel method for performing an iterative multidimensional hyperparameter search is presented, which resulted in significantly improved performance when applied to this dataset. Four iterations were conducted, which resulted in excellent 0.96 for metrics of precision, recall, and f1. Due to this strong performance, the food crisis year was removed from the dataset to prevent immediate extrapolation when used on future data, and the modeling process was repeated. The best “no year” model metrics remained strong, achieving ≥0.92 for recall, precision, and f1 while meeting a 10% f1 overfitting threshold on the test (0.84) and holdout (0.83) datasets. The year-agnostic neural network model represents a novel approach to classify food crises and outperforms current food crisis prediction efforts.
Model For Quantifying The Quality Of Secure Service, Paul M. Simon, Scott R. Graham, Christopher Talbot, Micah J. Hayden
Model For Quantifying The Quality Of Secure Service, Paul M. Simon, Scott R. Graham, Christopher Talbot, Micah J. Hayden
Faculty Publications
Although not common today, communications networks could adjust security postures based on changing mission security requirements, environmental conditions, or adversarial capability, through the coordinated use of multiple channels. This will require the ability to measure the security of communications networks in a meaningful way. To address this need, in this paper, we introduce the Quality of Secure Service (QoSS) model, a methodology to evaluate how well a system meets its security requirements. This construct enables a repeatable and quantifiable measure of security in a single- or multi-channel network under static configurations. In this approach, the quantification of security is based …
Defect Detection In Atomic Resolution Transmission Electron Microscopy Images Using Machine Learning, Philip Cho, Aihua W. Wood, Krishnamurthy Mahalingam, Kurt Eyink
Defect Detection In Atomic Resolution Transmission Electron Microscopy Images Using Machine Learning, Philip Cho, Aihua W. Wood, Krishnamurthy Mahalingam, Kurt Eyink
Faculty Publications
Point defects play a fundamental role in the discovery of new materials due to their strong influence on material properties and behavior. At present, imaging techniques based on transmission electron microscopy (TEM) are widely employed for characterizing point defects in materials. However, current methods for defect detection predominantly involve visual inspection of TEM images, which is laborious and poses difficulties in materials where defect related contrast is weak or ambiguous. Recent efforts to develop machine learning methods for the detection of point defects in TEM images have focused on supervised methods that require labeled training data that is generated via …
Aircraft Inspection By Multirotor Uav Using Coverage Path Planning, Patrick H. Silberberg
Aircraft Inspection By Multirotor Uav Using Coverage Path Planning, Patrick H. Silberberg
Theses and Dissertations
All military and commercial aircraft must undergo frequent visual inspections in order to identify damage that could pose a danger to safety of flight. Currently, these inspections are primarily conducted by maintenance personnel. Inspectors must scrutinize the aircraft’s surface to find and document defects such as dents, hail damage, broken fasteners, etc.; this is a time consuming, tedious, and hazardous process. The goal of this work is to develop a visual inspection system which can be used by an Unmanned Aerial Vehicle (UAV), and to test the feasibility of this system on military aircraft. Using an autonomous system in place …
Long Distance Bluetooth Low Energy Exploitation On A Wireless Attack Platform, Stephanie L. Long
Long Distance Bluetooth Low Energy Exploitation On A Wireless Attack Platform, Stephanie L. Long
Theses and Dissertations
In the past decade, embedded technology, known as the Internet of Things, has expanded for many uses. The smart home infrastructure has drastically grown to include networked refrigerators, lighting systems, speakers, watches, and more. This increase in the use of wireless protocols provides a larger attack surface for cyber actors than ever before. Wireless loT traffic is susceptible for sniffing by an attacker. The attack platform skypie is upgraded to incorporate Bluetooth Low Energy (BLE) beacon collection for pattern-of-life data, as well as device characteristic enumeration and potential characteristic modification. This platform allows an attacker to mount the skypie to …
Indoor Navigation Using Convolutional Neural Networks And Floor Plans, Ricky D. Anderson
Indoor Navigation Using Convolutional Neural Networks And Floor Plans, Ricky D. Anderson
Theses and Dissertations
The goal of this thesis is to evaluate a new indoor navigation technique by incorporating floor plans along with monocular camera images into a CNN as a potential means for identifying camera position. Building floor plans are widely available and provide potential information for localizing within the building. This work sets out to determine if a CNN can learn the architectural features of a floor plan and use that information to determine a location. In this work, a simulated indoor data set is created and used to train two CNNs. A classification CNN, which breaks up the floor plan into …
Solving The Quantum Layout Problem For Nisq-Era Quantum Computers Via Metaheuristic Algorithms, Brian D. Curran Jr.
Solving The Quantum Layout Problem For Nisq-Era Quantum Computers Via Metaheuristic Algorithms, Brian D. Curran Jr.
Theses and Dissertations
In the noisy intermediate-scale quantum (NISQ)-era, quantum computers (QC) are highly prone to noise-related errors and suffer from limited connectivity between their physical qubits. Circuit transformations must be made to abstract circuits to address the noise and hardware constraints of NISQ-era devices. Such transformations introduce additional gates to the original circuit, thereby reducing the circuit's overall fidelity. To address the aforementioned constraints of NISQ-era QCs, dynamic remapping procedures permute logical qubits about physical qubits of the device to increase the fidelity of operations and make operations hardware-compliant. The quantum layout problem (QLP) is the problem of mapping logical qubits of …
Delayed Authentication System For Civilian Satellite, Sean M. Feschak
Delayed Authentication System For Civilian Satellite, Sean M. Feschak
Theses and Dissertations
This thesis presents the feasibility of a Delayed Authentication System (DAS) for civilian satellite navigation (satnav) receivers. In satnav systems, encrypted signal components are transmitted synchronously with civilian components. Hence, the civilian signals can be authenticated by detecting the presence of encrypted signal components within the received signal. To authenticate, a reference station transmits estimated encrypted signal spreading code symbols processed using a high gain antenna. In this thesis, it is shown that a 1-meter diameter dish antenna is adequate to provide a high probability of successful authentication, thereby reducing overall system complexity and cost.
Comparison Of Machine Learning Techniques On Trust Detection Using Eeg, James R. Elkins
Comparison Of Machine Learning Techniques On Trust Detection Using Eeg, James R. Elkins
Theses and Dissertations
Trust is a pillar of society and is a fundamental aspect in every relationship. With the use of automated agents in todays workforce exponentially growing, being able to actively monitor an individuals trust level that is working with the automation is becoming increasingly more important. Humans often have miscalibrated trust in automation and therefore are prone to making costly mistakes. Since deciding to trust or distrust has been shown to correlate with specific brain activity, it is thought that there are EEG signals which are associated with this decision. Using both a human-human trust and a human-machine trust EEG dataset …
Improving Text Classification With Semantic Information, Joshua H. White
Improving Text Classification With Semantic Information, Joshua H. White
Theses and Dissertations
The Air Force contracts a variety of positions, from Information Technology to maintenance services. There is currently no automated way to verify that quotes for services are reasonably priced. Small training data sets and word sense ambiguity are challenges that such a tool would encounter, and additional semantic information could help. This thesis hypothesizes that leveraging a semantic network could improve text-based classification. This thesis uses information from ConceptNet to augment a Naive Bayes Classifier. The leveraged semantic information would add relevant words from the category domain to the model that did not appear in the training data. The experiment …
Unsupervised Clustering Of Rf-Fingerprinting Features Derived From Deep Learning Based Recognition Models, Christian T. Potts
Unsupervised Clustering Of Rf-Fingerprinting Features Derived From Deep Learning Based Recognition Models, Christian T. Potts
Theses and Dissertations
RF-Fingerprinting is focus of machine learning research which aims to characterize wireless communication devices based on their physical hardware characteristics. It is a promising avenue for improving wireless communication security in the PHY layer. The bulk of research presented to date in this field is focused on the development of features and classifiers using both traditional supervised machine learning models as well as deep learning. This research aims to expand on existing RF-Fingerprinting work by approaching the problem through the lens of an unsupervised clustering problem. To that end this research proposes a deep learning model and training methodology to …
Error Detection In Quantum Algorithms, Simeon R. Hanks
Error Detection In Quantum Algorithms, Simeon R. Hanks
Theses and Dissertations
Quantum computers need to be able to control highly entangled quantum states in the presence of environmental perturbations that lead to errors in calculations. Progress in superconducting qubits has enabled the development of computers capable of running small quantum circuits. The current era of Noise Intermediate Scale Quantum computing has a high error rate. To alleviate this error rate we apply an encoding scheme that allows us to remove results with known errors improving the quality of our results. The encoding uses multiple qubits as a single logical qubit and balances the natural tendency of state-of-the-art quantum computers to decohere …
Amplitude Estimation For The Large Clutter Discrete Removal Algorithm, Hannah Gjermo Chomitz
Amplitude Estimation For The Large Clutter Discrete Removal Algorithm, Hannah Gjermo Chomitz
Theses and Dissertations
A large clutter discrete (LCD) is spectrally bright localized clutter that can cause a false alarm or missed target detection in space-time adaptive processing (STAP) radar data. For passive bistatic STAP, the four step LCD removal (LCDR) algorithm estimates the spatial/Doppler frequency and complex amplitude of the LCD and then removes it from the data. Once the LCD is removed from the data, homogeneous clutter suppression techniques can be used to process the data and search for targets. This research focuses on reducing the complexity of estimating the LCDs complex amplitude. This research proposes a method that directly solves for …
Stereo Camera Calibrations With Optical Flow, Joshua D. Larson
Stereo Camera Calibrations With Optical Flow, Joshua D. Larson
Theses and Dissertations
Remotely Piloted Aircraft (RPA) are currently unable to refuel mid-air due to the large communication delays between their operators and the aircraft. AAR seeks to address this problem by reducing the communication delay to a fast line-of-sight signal between the tanker and the RPA. Current proposals for AAR utilize stereo cameras to estimate where the receiving aircraft is relative to the tanker, but require accurate calibrations for accurate location estimates of the receiver. This paper improves the accuracy of this calibration by improving three components of it: increasing the quantity of intrinsic calibration data with CNN preprocessing, improving the quality …
Accurate Covariance Estimation For Pose Data From Iterative Closest Point Algorithm, Rick H. Yuan
Accurate Covariance Estimation For Pose Data From Iterative Closest Point Algorithm, Rick H. Yuan
Theses and Dissertations
One of the fundamental problems of robotics and navigation is the estimation of relative pose of an external object with respect to the observer. A common method for computing the relative pose is the Iterative Closest Point (ICP) algorithm, where a reference point cloud of a known object is registered against a sensed point cloud to determine relative pose. To use this computed pose information in down-stream processing algorithms, it is necessary to estimate the uncertainty of the ICP output, typically represented as a covariance matrix. In this thesis a novel method for estimating uncertainty from sensed data is introduced. …
Enumerating And Locating Bluetooth Devices For Casualty Recovery In A First-Responder Environment, Justin M. Durham
Enumerating And Locating Bluetooth Devices For Casualty Recovery In A First-Responder Environment, Justin M. Durham
Theses and Dissertations
It is difficult for first-responders to quickly locate casualties in an emergency environment such as an explosion or natural disaster. In order to provide another tool to locate individuals, this research attempts to identify and estimate the location of devices that would likely be located on or with a person. A variety of devices, such as phones, smartwatches, and Bluetooth-enabled locks, are tested in multiple environments and at various heights to determine the impact that placement and interference played in locating the devices. The hypothesis is that most Bluetooth devices can be successfully enumerated quickly, but cannot be accurately located …
Cyclic Pursuit, Daniel E. Oke
Cyclic Pursuit, Daniel E. Oke
Theses and Dissertations
This thesis analyzes cyclic pursuit with the intent of developing swarm attack strategies for autonomous agents. Research was focused on finding the effects of pursuers capture range, evader speed and size of formation on the probability of escape. The temporal evolution of several polygonal formations was analyzed. The polygons could be regular or arbitrary polygons. The thesis demonstrated that an increased capture range, formation size, reduced evader speed aided capture probability. Irregular n-gon formations reduced to n-1 gon repeatedly, pursuer clusters formed until two clusters remained which eventually came together, so all the n pursuers coalesced until convergence. Regular n-gon …