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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 …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
Designing And Building A Radar Simulation Using The Entity Component System, Brennen T. Garland
Designing And Building A Radar Simulation Using The Entity Component System, Brennen T. Garland
Theses and Dissertations
This research explores the implementation of a "medium fidelity" radar simulation using the Entity-Component-System (ECS) architecture. The radar implemented mimics the fundamental characteristics of entities in the open-source Mixed Reality Simulation Platform (MIXR) project, supporting real-time interaction. Previous research has shown the potential benefits of using an ECS-based architecture to support improved execution performance relative to Object-Oriented Programming (OOP) approaches, thus improved real-time interaction requirements. This research implements a well-documented radar model that supports the development of soft real-time human-based interaction simulations. The radar system modeled in this research mimics the "out-of-the-box" fidelity defined in the OOP-based MIXR architecture. This …
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. …
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 …
Infiniband Network Monitoring: Challenges And Possibilities, Kyle D. Hintze
Infiniband Network Monitoring: Challenges And Possibilities, Kyle D. Hintze
Theses and Dissertations
Within the realm of High Performance Computing, the InfiniBand Architecture is among the leading interconnects used today. Capable of providing high bandwidth and low latency, InfiniBand is finding applications outside the High Performance Computing domain. One of these is critical infrastructure, encompassing almost all essential sectors as the work force becomes more connected. InfiniBand is not immune to security risks, as prior research has shown that common traffic analyzing tools cannot effectively monitor InfiniBand traffic transmitted between hosts, due to the kernel bypass nature of the IBA in conjunction with Remote Direct Memory Access operations. If Remote Direct Memory Access …
A Framework For Autonomous Cooperative Optimal Assignment And Control Of Satellite Formations, Devin E. Saunders
A Framework For Autonomous Cooperative Optimal Assignment And Control Of Satellite Formations, Devin E. Saunders
Theses and Dissertations
A decentralized, cooperative multi-agent optimal control framework is presented to offer a solution to the assignment and control problems associated with performing multi-agent tasks in a proximity operations environment. However, the framework developed may be applied to a variety domains such as air, space, and sea. The solution presented takes advantage of a second price auction assignment algorithm to optimally task each satellite, while model predictive control is implemented to control the agents optimally while adhering to safety and mission constraints. The solution is compared to a pseudospectral collocation method, and a study on tuning parameters is included.
Display Design To Avoid And Mitigate Limit Cycle Oscillations (Lco) On The F-16c, David J. Feibus
Display Design To Avoid And Mitigate Limit Cycle Oscillations (Lco) On The F-16c, David J. Feibus
Theses and Dissertations
The U.S. Air Force F-16 Fighting Falcons flying characteristics and flight envelope are dynamic and defined by its external weapon stores configuration. The employment of its munitions at certain speeds can put the F-16 into a flutter-like state in which Limit Cycle Oscillations (LCO) are induced. In LCO, a pilots fine motor control might be hindered, and the aircraft may lose combat effectiveness until flight conditions are reduced. The current research attempted to provide pilots with a predictive feedback display to avoid an LCO-susceptible configuration by increasing their situation awareness about the consequences of employing certain munitions to their flight …
Enhanced Space Object Detection Without Prior Knowledge Of The Point Spread Function, Grant F. Graupmann
Enhanced Space Object Detection Without Prior Knowledge Of The Point Spread Function, Grant F. Graupmann
Theses and Dissertations
Since the point detector was created, other detection algorithms have been created that increase the probability of detection, while still keeping the same probability of false alarm. The point detector still has uses, such as when there is no prior knowledge of the point spread function (PSF). The matched filter correlator (MFC) detector is reliant on prior knowledge of the PSF. This has been an issue in cases where the PSF information is potentially inaccurate or unknown. This thesis utilizes MFC detector in a manner that it has never been used before, along with a new detection algorithm, the Pearson's …
Anomaly Detection And Encrypted Programming Forensics For Automation Controllers, Robert W. Mellish
Anomaly Detection And Encrypted Programming Forensics For Automation Controllers, Robert W. Mellish
Theses and Dissertations
Securing the critical infrastructure of the United States is of utmost importance in ensuring the security of the nation. To secure this complex system a structured approach such as the NIST Cybersecurity framework is used, but systems are only as secure as the sum of their parts. Understanding the capabilities of the individual devices, developing tools to help detect misoperations, and providing forensic evidence for incidence response are all essential to mitigating risk. This thesis examines the SEL-3505 RTAC to demonstrate the importance of existing security capabilities as well as creating new processes and tools to support the NIST Framework. …
Automated Network Exploitation Utilizing Bayesian Decision Networks, Graeme M. Roberts
Automated Network Exploitation Utilizing Bayesian Decision Networks, Graeme M. Roberts
Theses and Dissertations
Computer Network Exploitation (CNE) is the process of using tactics and techniques to penetrate computer systems and networks in order to achieve desired effects. It is currently a manual process requiring significant experience and time that are in limited supply. This thesis presents the Automated Network Discovery and Exploitation System (ANDES) which demonstrates that it is feasible to automate the CNE process. The uniqueness of ANDES is the use of Bayesian decision networks to represent the CNE domain and subject matter expert knowledge. ANDES conducts multiple execution cycles, which build upon previous action results. Cycles begin by modeling the current …
Exploring Fog Of War Concepts In Wargame Scenarios, Dillon N. Tryhorn
Exploring Fog Of War Concepts In Wargame Scenarios, Dillon N. Tryhorn
Theses and Dissertations
This thesis explores fog of war concepts through three submitted journal articles. The Department of Defense and U.S. Air Force are attempting to analyze war scenarios to aid the decision-making process; fog modeling improves realism in these wargame scenarios. The first article "Navigating an Enemy Contested Area with a Parallel Search Algorithm" [1] investigates a parallel algorithm's speedup, compared to the sequential implementation, with varying map configurations in a tile-based wargame. The parallel speedup tends to exceed 50 but in certain situations. The sequential algorithm outperforms it depending on the configuration of enemy location and amount on the map. The …
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 …
Performance Of Various Low-Level Decoder For Surface Codes In The Presence Of Measurement Error, Claire E. Badger
Performance Of Various Low-Level Decoder For Surface Codes In The Presence Of Measurement Error, Claire E. Badger
Theses and Dissertations
Quantum error correction is a research specialty within the area of quantum computing that constructs quantum circuits that correct for errors. Decoding is the process of using measurements from an error correcting code, known as error syndrome, to decide corrective operations to perform on the circuit. High-level decoding is the process of using the error syndrome to perform corrective logical operations, while low-level decoding uses the error syndrome to correct individual data qubits. Research on machine learning-based decoders is increasingly popular, but has not been thoroughly researched for low-level decoders. The type of error correcting code used is called surface …
Laser Illuminated Imaging: Beam And Scene Deconvolution Algorithm, Benjamin W. Davis
Laser Illuminated Imaging: Beam And Scene Deconvolution Algorithm, Benjamin W. Davis
Theses and Dissertations
Laser illuminated imaging systems deal with several physical challenges that must be overcome to achieve high-resolution images of the target. Noise sources like background noise, photon counting noise, and laser speckle noise will all greatly affect the imaging systems ability to produce a high-resolution image. An even bigger challenge to laser illuminated imaging systems is atmospheric turbulence and the effect that it will have on the imaging system. The illuminating beam will experience tilt, causing the beam to wander off the center of the target during propagation. The light returning to the detector will similarly be affected by turbulence, and …