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Articles 121 - 150 of 3078
Full-Text Articles in Physical Sciences and Mathematics
Proximal Policy Optimization Applied To The Beyond Visual Range Air Combat Maneuvering Problem, Daniel B. Joseph
Proximal Policy Optimization Applied To The Beyond Visual Range Air Combat Maneuvering Problem, Daniel B. Joseph
Theses and Dissertations
Artificial intelligence (AI) grows ever-more important in warfighting. Emerging technologies allow for the use of AI to control aircraft and weapons systems. This research investigates the application of reinforcement learning (RL) through the Proximal Policy Optimization (PPO) algorithm to a two-versus-two (2v2) beyond-visual-range (BVR) air combat maneuvering problem (ACMP). Implemented in the Advanced Framework for Simulation, Integration, and Modeling (AFSIM), the methodology frames the engagement as a Markov decision process, wherein an autonomous RL agent learns continuous control decisions—throttle, pitch, roll, and yaw—under a cooperative communication scheme. A multi-phase curriculum-learning approach facilitates the progressive acquisition of flight stability, weapon deployment, …
Machine Learning Techniques To Predict Solar Particle Events And Radiation Of Aircrew, Haley Traub
Machine Learning Techniques To Predict Solar Particle Events And Radiation Of Aircrew, Haley Traub
Theses and Dissertations
Solar Particle Events (SPEs) are high-energy phenomena from the Sun that pose risks to technology, human health, and Air Force operations. Accurate prediction of SPEs exceeding 100 MeV is crucial for mitigating these risks. This thesis explores using Bayesian statistical models to predict such events, integrating prior knowledge from solar physics with the ability to update predictions based on new data. The research uses a dataset spanning three solar cycles (21–23) and incorporates attributes like flare fluence, peak flux, latitude, longitude, and class. Four Bayesian models (PyMC, Bnlearn, and two Dredge models) were compared to machine learning models. The Bayesian …
Cloud One Migration Duration And Its Drivers, Grayson T. Hall
Cloud One Migration Duration And Its Drivers, Grayson T. Hall
Theses and Dissertations
As modern warfare evolves with rapid technological advancements, cloud computing plays a critical role in managing the vast amounts of data required for real-time decision making, as well as enabling seamless organizational access to mission-critical programs and information from around the globe. Recognizing its importance, the Department of Defense (DoD) identified cloud computing as essential for maintaining the military’s technological edge. However, despite cloud computing’s strategic significance, the DoD faces challenges in successfully implementing department-wide cloud computing. In contrast, the Air Force’s cloud computing environment, Cloud One, is fully operational and has already integrated over 145 systems into its platform. …
Numerical Studies Of Semiclassical Light Storage Using The Coherent Atomic Transfer Function, Zachary T. Johnson
Numerical Studies Of Semiclassical Light Storage Using The Coherent Atomic Transfer Function, Zachary T. Johnson
Theses and Dissertations
Quantum communication through photons relies on photonic storage to preserve quantum states. However, when photons interact with matter, quantum information becomes distorted. A recently developed semi-classical analytical model predicts output light pulses from an electromagnetically induced transparency (EIT) system. Using the predictions, the model known as the Coherent Atomic Transfer (CAT) Function, is capable of predicting the stored pulse or reconstructing the original pulse. Using numerical convolution and deconvolution with the CAT function as an analog of the point spread function of Fourier optics can provide insights on the effects of EIT storage on the retrieved pulse. Blind deconvolution is …
Isotopic Analysis Of Lithium Hydroxide Monohydrate Using Laser-Induced Breakdown Self-Reversal Isotopic Spectrometry (Libris) And Machine Learning, Madison R. Moran
Isotopic Analysis Of Lithium Hydroxide Monohydrate Using Laser-Induced Breakdown Self-Reversal Isotopic Spectrometry (Libris) And Machine Learning, Madison R. Moran
Theses and Dissertations
High-resolution Laser-Induced Breakdown Self-Reversal Isotopic Spectrometry (LIBRIS) is implemented to record the 15.8 pm Li I 670.8 nm isotopic shift in LiOH · H2O samples of varying 6, 7Li abundance. A simple univariate linear regression demonstrates an acquired isotopic shift of 13.813 ± 1.21 pm in samples varying from 3 to 95 6Li at%. Supervised machine learning regressions are trained on self-reversal wavelength locations in order to quantify 6Li abundance. A stacked ensemble using two base learners yields the superlative characterization of 6Li abundance with RMSE of 5.66 at% and detection limit of 18.8 at%. Using …
Transfer Efficiencies Of Surface-To-Surface Transport Of Micron-Sized Actinide Surrogate Particles, Austin R. Powell
Transfer Efficiencies Of Surface-To-Surface Transport Of Micron-Sized Actinide Surrogate Particles, Austin R. Powell
Theses and Dissertations
Particle effluent of varying sizes is released during routine activities within laboratory environments and these particles can come in contact with a wide range of surfaces. Particles of micron size or smaller can be especially pervasive and can transfer between multiple subsequent surfaces, leading to the progressive contamination of a laboratory. Understanding the transport dynamics of micron sized particles will help inform facility personnel of the possibility of contamination by these potentially hazardous materials of interest. In this research, the transfer efficiency of micron sized surrogate actinide particles (Europium-doped Gadolinium Oxysulfide (EGOS)) is measured for multiple materials, between two particle …
Reflected Laser Light As A Diagnostic Insight For Femtosecond Laser-Plasma Coupling, David S. Stiles
Reflected Laser Light As A Diagnostic Insight For Femtosecond Laser-Plasma Coupling, David S. Stiles
Theses and Dissertations
High-energy laser technology is critical for military, scientific, and industrial applications, but traditional mixed-radiation facilities are hindered by cost, scheduling constraints, and lack of portability. A high-repetition-rate, cost-effective alternative is needed to meet evolving application needs and timelines. This study investigates changes in reflected laser intensity to diagnose coupling efficiency and optimize energetic particle generation in ultraintense laser-target interactions. Using an ultraintense 35 femtosecond, 9 - 12 mJ laser and a deuterated water target, reflected laser light was captured via high-resolution imaging, while energetic electron and X-ray data were collected using a custom spectrometer and ion chamber, respectively. Statistical analysis …
Grassmannian Codes From Stratified Frames, William J. Brinkley
Grassmannian Codes From Stratified Frames, William J. Brinkley
Theses and Dissertations
An equichordal tight fusion frame (ECTFF) is a finite sequence of equi-dimensional subspaces of a Euclidean space that achieves equality in Conway, Hardin and Sloane's simplex bound. Every ECTFF is an optimal Grassmannian code with respect to the chordal distance. We introduce a method for constructing an ECTFF from any finite sequence of unit norm tight frames that happen to be stratified in a certain sense. We moreover show how to construct stratified unit norm tight frames from a difference family for a finite abelian group, as well as from a suitable combination of a resolvable balanced incomplete block design …
Implications Of Magnetic Evolution On Coronal Loop Thermal Variation Prior To Solar Flares, Kara L. Kniezewski
Implications Of Magnetic Evolution On Coronal Loop Thermal Variation Prior To Solar Flares, Kara L. Kniezewski
Theses and Dissertations
Solar flares are intense bursts of electromagnetic radiation, which occur due to a rapid destabilization and reconnection of the magnetic field. While flares are a magnetic phenomena, very little attention has been paid to thermal conditions in the corona prior to flare onset. This study serves as a follow-on to Kniezewski et al., 2024, where the EUV emission from coronal loops was observed to vary substantially and without any coherence between channels before 53 off-limb flares. These variations suggest multiple mechanisms within the coronal magnetic field are responsible for heating fluctuations. Here, the 3D magnetic field is modeled using a …
Investigation Of Neutron Inelastic Scatter Cross Sections On 16O, Molly A. Wakeling
Investigation Of Neutron Inelastic Scatter Cross Sections On 16O, Molly A. Wakeling
Theses and Dissertations
Nuclear data underpin a number of applications across nuclear reactor design, medicine, astrophysics, nonproliferation, national security, and other related fields. However, data are conflicting or missing across the spectrum of isotopes and reactions, and theoretical calculations can only go so far to predict nuclear properties. A key reaction with incomplete data is neutron inelastic scatter on 16O, which reduces neutron energies and produces high-energy gamma rays that are often not taken into account in simulations of nuclear reactors, nuclear weapon detonations, nuclear fusion, and other areas. To fill these gaps, the Gamma-Energy Neutron-Energy Spectrometer for Inelastic Scattering, or GENESIS, …
Assessing The Feasibility Of Managed Aquifer Recharge For The United States Air Force, Daniel Hendrix
Assessing The Feasibility Of Managed Aquifer Recharge For The United States Air Force, Daniel Hendrix
Theses and Dissertations
Water stress is becoming an increasing global issue, with 4 billion people (50% of the world’s population) experiencing water stress at least one month per year. By 2050, 60% of the world’s population and $70 trillion USD in global gross domestic product will be affected. This research analyzes 78 CONUS USAF installations to determine location-specific water stress and feasible Managed Aquifer Recharge (MAR) solutions. Although thousands of MAR projects have been implemented globally, active-duty USAF installations have yet to contribute to solving this growing issue. Important factors such as required subsurface conditions, physical limitations, design, cost, and regulatory constraints are …
Event-Based Camera Simulation And Neural Network Processing For Autonomous Aerial Refueling, Stephanie C. Hanson
Event-Based Camera Simulation And Neural Network Processing For Autonomous Aerial Refueling, Stephanie C. Hanson
Theses and Dissertations
Event-based cameras excel in dynamic environments, and do not face challenges like washout and motion blur, like a frame-based camera. This work describes the process used to collect the first EBS data collect for use in AAR, and develops an event simulator to generate synthetic training data for evaluating CNN architectures on asynchronous data. The three models compared are a traditional CNN, a YOLO-based CNN, and an asynchronous sparse CNN. The YOLO-based model achieved the best accuracy, while the sparse CNN, despite being less optimized, maintained an average IoU of 0.9. These results highlight the potential of asynchronous approaches for …
Heterogeneous Boid Swarm Performance Under Environmental And Neighbor Communication Link Variability, Jonathan C. Oxborrow
Heterogeneous Boid Swarm Performance Under Environmental And Neighbor Communication Link Variability, Jonathan C. Oxborrow
Theses and Dissertations
Artificial swarms are of growing interest in numerous fields and use cases. As their utilization increases drones and robots with different capabilities will be required to coordinate for task completion thus creating heterogeneous swarms. Swarm individuals generally communicate with all neighbors inside their sensor range generating a significant amount of message traffic. Previous research of a heterogeneous group in a non-physical environment has shown that restricting communication to only one neighbor of each different capability maintained performance. This work applies that finding to a heterogeneous boid swarm with the addition of varied environmental conditions. The swarm is comprised of three …
Analyzing Lf/Vlf Lightning Waveforms To Estimate D-Region Electron Density Profiles: Process, Application, And Comparison, William R. Smith
Analyzing Lf/Vlf Lightning Waveforms To Estimate D-Region Electron Density Profiles: Process, Application, And Comparison, William R. Smith
Theses and Dissertations
Lightning waveforms in the low frequency (LF; 30-300 kHz) and the very low frequency (VLF; 3-30 kHz) can be exploited to produce data-driven ionospheric Dregion electron density profile (EDP) estimates with significantly higher spatial and temporal coverage than previously available. The lightning waveforms used in this paper are signals detected in the LF/VLF of negative cloud-to-ground lightning by the Earth Networks Total Lightning Detection Network. Each waveform contains a ground wave and a time-delayed ionospheric reflection. The time delay between the ground wave and ionospheric reflection has previously been used to estimate a single specular reflection altitude, where LF/VLF emissions …
Forecasting Army Recruiting Mission Distribution, Maxwell A. Deihl
Forecasting Army Recruiting Mission Distribution, Maxwell A. Deihl
Theses and Dissertations
The Army’s recruiting landscape has changed markedly in recent years, raising questions about whether forecasting methods of Army contracts remain robust. This thesis recreates the presented models in Joshua McDonald’s 2015 thesis. It replicates and evaluates the models with updated data (2018–2023) to assess their current validity and compare them to novel alternative approaches, such as simpler regression models or neural networks. While the 2015 model remains a valuable baseline, results suggest that either refining its variables or adopting alternative methods can improve predictive accuracy and interpretability. Ultimately, the United States Army Recruiting Command has many options regarding how it …
Correlating Aerosol Morphology And Optical Properties With Image Blur: An Investigation Of Aerosol-Induced Image Degradation, Patricia A. Byrd
Correlating Aerosol Morphology And Optical Properties With Image Blur: An Investigation Of Aerosol-Induced Image Degradation, Patricia A. Byrd
Theses and Dissertations
Small-angle scattering by relatively large cloud/fog droplets and ice crystals is known to degrade imagery. However, it is not well investigated how much of an impact aerosols, especially the prevalent anthropogenic fine/ultra-fine/coarse particles, have in image degradation. This thesis explores the contribution of aerosols to image blur but focuses on collecting field data with a relatively large variety of ambient aerosol characterization and optical instrumentation. Field experiments were conducted over six days, correlating aerosol measurements (particle counters and nephelometer) with image quality from a visible camera along a 450m path. Image blur was quantified using the Modulation Transfer Function (MTF), …
A Standardized Methodology For Evaluating A Digital Badging System [ Data Package ], Benjamin T. Pederson, Mark G. Reith, Ralucca Gera, David S. Long, Edward D. White, Jonathan Zemmer
A Standardized Methodology For Evaluating A Digital Badging System [ Data Package ], Benjamin T. Pederson, Mark G. Reith, Ralucca Gera, David S. Long, Edward D. White, Jonathan Zemmer
Faculty Publications
Digital badges, a form of micro-credentials, have grown in popularity over the past decade. However, few standard processes exist to assess the potential of digital badging systems within an organization. This study proposes a generalizable methodology for comparing a badging system with other methods of recording skills and competencies. The experimental design is tested using the military's cyber operations community as the target organization. Finally, mixed-method data from thirty-six participants is analyzed in accordance with the methodology. Based on the results, digital badging systems are perceived to be more valuable and usable than a current method of military talent management. …
Convergent Close-Coupling Approach To Electron Impact Dissociation Of The Polyatomic Molecule H 3 + And Its Isotopologues, Reese K. Horton, Michael V. Pak, Igor Bray, Dmitry V. Fursa
Convergent Close-Coupling Approach To Electron Impact Dissociation Of The Polyatomic Molecule H 3 + And Its Isotopologues, Reese K. Horton, Michael V. Pak, Igor Bray, Dmitry V. Fursa
Faculty Publications
Cross sections for electron impact dissociative excitation and ionization in scattering on vibrationally excited levels of the ground electronic state of H3+ and its isotopologues are reported in the energy range of 8 to 1000 eV. Calculations have been performed using a newly developed version of the molecular convergent close-coupling code. Cross sections for total dissociative excitation, ionization yielding atomic fragments such as D+, and the total inelastic cross section are presented. Good agreement with available experiments has been demonstrated.
Convergent Close-Coupling Approach To Electron Scattering On H3+ : Scattering Dynamics And Dissociative Processes, Reese K. Horton, Michael V. Pak, Igor Bray, Dmitry V. Fursa
Convergent Close-Coupling Approach To Electron Scattering On H3+ : Scattering Dynamics And Dissociative Processes, Reese K. Horton, Michael V. Pak, Igor Bray, Dmitry V. Fursa
Faculty Publications
Cross sections for electron impact dissociative excitation and ionization in scattering on vibrationally excited levels of the ground electronic state of H3+, D3+, and T3+ are reported in the energy range of 8–1000 eV. Calculations have been performed using a newly developed version of the molecular convergent close-coupling code. Convergence of the cross sections with the size of the projectile partial-wave and close-coupling expansions is examined. Branching ratios and cross sections for the yields of D2+ and D+ from dissociative excitation of D3+ are presented and isotope …
Accelerated Multiobjective Calibration Of Fused Deposition Modeling 3d Printers Using Multitask Bayesian Optimization And Computer Vision, Craig S. Ganitano, Benji Maruyama, Gilbert L. Peterson
Accelerated Multiobjective Calibration Of Fused Deposition Modeling 3d Printers Using Multitask Bayesian Optimization And Computer Vision, Craig S. Ganitano, Benji Maruyama, Gilbert L. Peterson
Faculty Publications
Proper process parameter calibration is critical to the success of fused deposition modeling (FDM) three-dimensional (3D) printing, but is time-consuming and requires expertise. While existing systems for autonomous calibration have demonstrated success in calibrating for a single objective, users may need to balance multiple conflicting objectives. Herein, an easily deployable, camera-based system for autonomous calibration of FDM printers that optimizes for both part quality and completion time is presented. Autonomous calibration is achieved through a novel, multifaceted computer vision characterization and a multitask learning extension to Bayesian optimization. The system is demonstrated on four popular filament types using two distinct …
Display System Interface Using Visually-Evoked Cortical Potentials, Michael E. Miller, Brett J. Borghetti, Kellie D. Kennedy, Chad L. Stephens, Alan T. Pope
Display System Interface Using Visually-Evoked Cortical Potentials, Michael E. Miller, Brett J. Borghetti, Kellie D. Kennedy, Chad L. Stephens, Alan T. Pope
AFIT Patents
A brain-computer interface system includes a video processor for producing a display signal, a temporal controller for producing a plurality of repetitive visual stimulus (RVS) signals with different respective temporal aspects, a display device that receives the display signal and displays a corresponding image on a plurality of different display regions and receives the RVS signals and displays corresponding RVS in respective ones of the display regions, an electroencephalographic (EEG) sensor for sensing a visually-evoked cortical potential (VECP) signal in a user with eyes fixated on a viewed one of the display regions, and a VECP processor for processing the …
Navigating Together: The Conav Testbed And Framework For Benchmarking Cooperative Localization, Rohith Boyinine, Jayanth Ammapalli, Anusna Chakraborty, Rajnikant Sharma, Kevin Brink, Clark N. Taylor
Navigating Together: The Conav Testbed And Framework For Benchmarking Cooperative Localization, Rohith Boyinine, Jayanth Ammapalli, Anusna Chakraborty, Rajnikant Sharma, Kevin Brink, Clark N. Taylor
Faculty Publications
This paper presents CoNaV, a comprehensive framework for creating a multi-vehicle cooperative localization (CL) testbed designed to support the benchmarking, development, and deployment of cooperative navigation algorithms. Given the essential role of CL in improving localization accuracy for both defense and civilian applications, CoNaV provides a robust environment for rigorously validating algorithms under real-world conditions. By establishing a benchmark for CL algorithms, CoNaV lays a foundation for advancing research into more sophisticated and distributed CL solutions. This framework highlights the potential of cooperative navigation to enhance multi-vehicle operations and offers a scalable, practical approach for future developments in CL technology.
Afit Generative Ai Teaching Guidebook, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer
Afit Generative Ai Teaching Guidebook, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer
AFIT Documents
AFIT is proud to highlight the Generative AI Teaching Guidebook, a resource designed to provide military educators with practical insights, strategies, and use cases for integrating Generative AI (Gen AI) into their teaching practices. Developed through a collaborative effort involving AFIT faculty across various departments within the Graduate School of Engineering and Management and the School of Systems and Logistics, this digital resource serves as a starting point for educators exploring how to leverage Gen AI in their classrooms. It offers accessible examples and best practices, ensuring utility for instructors of all technical backgrounds. The guidebook provides a comprehensive overview …
Afit Generative Ai Teaching Guidebook Synopsis, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer
Afit Generative Ai Teaching Guidebook Synopsis, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer
AFIT Documents
The main objective of this work was to bring together various perspectives on how to envision incorporating Gen AI capabilities into the learning environment and identify some best practices for their implementation. Any instructor who is interested in these capabilities but does not necessarily have a technical background can find pragmatic use of the examples provided. While the examples have a wide range of applicability, they are meant to serve as a starting point for educators to explore what would be beneficial to their educational environment, from traditional classroom settings to online continuing education courses.
Editorial: Artificial Intelligence For Smart Health: Learning, Simulation, And Optimization, Bing Yao, Nathan Gaw, Hyo Kyung Lee
Editorial: Artificial Intelligence For Smart Health: Learning, Simulation, And Optimization, Bing Yao, Nathan Gaw, Hyo Kyung Lee
Faculty Publications
With rapid developments in medical sensing and imaging, we now live in an era of data explosion in which large amounts of data are readily available in clinical environments. The fast-growing biomedical and healthcare data provide unprecedented opportunities for data-driven scientific knowledge discovery and clinical decision support. Our Research Topic aims to catalyze synergies among biomedical informatics, machine learning, computer simulation, operations research, systems engineering, and other related fields with three specific goals: (1) develop cutting-edge data-driven models to accelerate scientific knowledge discovery in biomedicine using healthcare data collected from laboratory systems, imaging systems, and medical and sensing devices; (2) …
Ultraviolet And Blue Stimulated Emission From Cs Alkali Vapor Pumped Using Two-Photon Absorption And Four-Wave Mixing, Ricardo C. Davila, Christopher A. Rice, Nathan B. Terry, Glen P. Perram
Ultraviolet And Blue Stimulated Emission From Cs Alkali Vapor Pumped Using Two-Photon Absorption And Four-Wave Mixing, Ricardo C. Davila, Christopher A. Rice, Nathan B. Terry, Glen P. Perram
Faculty Publications
Stimulated emission on the ultraviolet and blue transitions in Cs has been achieved by pumping via two-photon absorption and four-wave mixing for the pump transition 62S½ → 82D3/2,5/2. The emission performance of the optically pumped cesium vapor laser operating in ultraviolet and blue has been extended to 650 nJ/pulse for 387 nm, 1 to 3 μ J / pulse for 388 nm, 200 nJ/pulse for 455 nm, and 500 nJ/pulse for 459 nm. Emission performance improves dramatically as the cesium vapor density is increased, and no scaling limitations associated with energy pooling or …
Machine Visual Perception From Sim-To-Real Transfer Learning For Autonomous Docking Maneuvers, Derek Worth, Jeffrey Choate, Ryan M. Raettig, Scott L. Nykl, Clark N. Taylor
Machine Visual Perception From Sim-To-Real Transfer Learning For Autonomous Docking Maneuvers, Derek Worth, Jeffrey Choate, Ryan M. Raettig, Scott L. Nykl, Clark N. Taylor
Faculty Publications
This paper presents a comprehensive approach to enhancing autonomous docking maneuvers through machine visual perception and sim-to-real transfer learning. By leveraging relative vectoring techniques, we aim to replicate the human ability to execute precise docking operations. Our study focuses on autonomous aerial refueling as a use case, demonstrating significant advancements in relative navigation and object detection. We introduce a novel method for aligning digital twins using fiducial targets and motion capture data, which facilitates accurate pose estimation from real-world imagery. Additionally, we develop cost-efficient annotation automation techniques for generating high-quality You Only Look Once training data. Experimental results indicate that …
Temporal Metadata Analysis: A Learning Classifier System Approach, Michael C. Todd, Gilbert L. Peterson
Temporal Metadata Analysis: A Learning Classifier System Approach, Michael C. Todd, Gilbert L. Peterson
Faculty Publications
Digital forensics is a complex field that requires expert knowledge (EK) and specialized tools to collect, analyze, and report on digital evidence. Temporal metadata analysis is particularly challenging, requiring expert knowledge to understand and interpret underlying traces and associate them with their source. This paper introduces Digital Trace Inspector (DTI), a Learning Classifier System (LCS)-based decision support tool for temporal metadata analysis. DTI leverages a binary Michigan-style LCS to locate and group corroborating temporal digital traces of targeted user activity. Rules are built from expert-created atomics encoded as feature vectors using patterns defined in a structured EK rule framework. The …
Mitigating Code Reuse Attacks On Risc-V Binaries: Minimizing Gadget Availability Using The Compressed Extension, Heitor Vieira
Mitigating Code Reuse Attacks On Risc-V Binaries: Minimizing Gadget Availability Using The Compressed Extension, Heitor Vieira
Theses and Dissertations
Embedded systems are vital in civilian and military applications, requiring high performance and security. The open RISC-V Instruction Set Architecture (ISA) offers significant advantages, including security through community review and strategic independence in microchip supplies. Brazil’s recent partnership with RISC-V highlights its potential for national technological sovereignty. However, RISC-V is not inherently resistant to code reuse attacks (CRAs), highlighting the need to integrate security measures early in development. The RISC-V Compressed extension, while beneficial for optimizing performance and code flexibility, introduces security trade-offs. As RISC-V adoption grows, particularly in critical systems, addressing these security challenges from the start is crucial …
Dual-Channel Side Channel Attack: Improved Aes Key Decryption By Combining Power And Electromagnetic Side Channels With Convolutional Neural Networks, Sean P. O'Neill
Dual-Channel Side Channel Attack: Improved Aes Key Decryption By Combining Power And Electromagnetic Side Channels With Convolutional Neural Networks, Sean P. O'Neill
Theses and Dissertations
This research introduces a novel DL approach for SCA that combines power consumption and EM signals to enhance encryption key deduction by leveraging a dual-channel CNN architecture. A new dataset, consisting of simultaneous power and EM signal collections during 128-bitAES encryption, was developed to train and evaluate the model’s effectiveness. The combined approach achieved an 88% reduction in traces needed, from 50 traces to 6, for encryption key classification, outperforming traditional methods such as random forest, DPA, DEMA,and individual side channel CNN models. These findings highlight the potential of integrating multiple side channels in SCA to improve performance without the …