Evaluating A Military Digital Badging System Prototype,
2025
Air Force Institute of Technology
Evaluating A Military Digital Badging System Prototype, Benjamin T. Pederson
Theses and Dissertations
The Department of Defense is committed to developing and maintaining a highly skilled workforce capable of defending the United States and associated interests abroad. Digital badging systems, a form of micro-credentialing, offer a way to record service member competencies. By providing decision-makers with granular data, this technology could augment the military’s development of a highly skilled workforce, especially in technical career fields including cyber operations. Mixed-method data from thirty-six participants suggest that establishing a digital badging program could increase deterrence and operational effectiveness.
Jamming-Tolerant Low-Rate Wireless Personal Area Network For Detection Sensor Networks,
2025
Air Force Institute of Technology
Jamming-Tolerant Low-Rate Wireless Personal Area Network For Detection Sensor Networks, Michael A. Eddy
Theses and Dissertations
This research evaluates the impact of electronic warfare, particularly jamming, on an audio-based drone detection wireless sensor network (WSN) using Monte Carlo simulations. A six-node IEEE 802.15.4 network, with five edge nodes and a central sink, is tested against jamming probabilities ranging from 0-100% in 5% increments across 30 iterations per configuration. Results show that packet delivery ratio (PDR) degrades linearly at approximately 20% per jammed node, while detection performance often exceeds PDR. Even at 80% jamming, detection success rates remain above 57%, highlighting resilience despite network degradation. The study reveals that jamming effectiveness depends on node placement relative to …
Out-Of-Band Anomaly Detection For Real Time Operating Systems,
2025
University of South Alabama
Out-Of-Band Anomaly Detection For Real Time Operating Systems, Jeff K. Holifield
Shelby Hall Graduate Research Forum Posters
Real Time Operating Systems (RTOS) are increasing present throughout the industrial, business, defense, and healthcare spaces. These lightweight and efficient operating systems are designed to run on embedded, resource constrained devices, often within cyber-physical systems (CFS). A defining characteristic of RTOSs is that they are deterministic. Tasks are scheduled to run on fixed timelines within guaranteed execution windows. To accomplish tasks on time, real time software must conform to worst case execution times (WCETs) as design parameters. WCET is the maximum time a particular task can take to complete. Exceeding the WCET could cause system failure and lead to damage, …
Analysis Of Forensic Techniques For Additive Manufacturing Devices,
2025
University of South Alabama
Analysis Of Forensic Techniques For Additive Manufacturing Devices, Daniel B. Miller, Brad Glisson, Mark Yampolskiy, J Todd Mcdonald
Shelby Hall Graduate Research Forum Posters
Additive Manufacturing (AM) is a set of newer computer-dependent production technologies that is seeing rapid adoption across a wide variety of industries, including defense, aerospace, automotive, and healthcare. With increased adoption comes an increased opportunity for misuse and abuse of such systems, which will lead to an increased need for Digital Forensic investigations into these platforms. This research forensically analyzes a number of AM devices to explore the options available for data acquisition as well as the impacts of hardware and software design choices on the analysis and investigation results. Hardware is investigated using Open-Source Intelligence (OSINT) sources to determine …
Using Image-Based Representation For Network Intrusion Detection,
2025
University of South Alabama
Using Image-Based Representation For Network Intrusion Detection, Chakriya Suon, J. Todd Mcdonald
Shelby Hall Graduate Research Forum Posters
The primary focus of our research is to evaluate the effectiveness of converting network traffic data, PCAPs, into image-based representations for anomaly-based network intrusion detection. We aim to analyze PCAPs to detect malware, or malicious software in hopes of creating a useful approach for anomaly detection against cyber threats including Advanced Persistent Threats (APTs). With the rise of cyber threats, cybersecurity continues to play a critical role in the ever-changing landscape of technology, by protecting and defending against threat agents. Our research will apply novel machine learning (ML) techniques to detect potential malware transmitted over a network effectively. The overall …
Security Vulnerabilities Of A Field Programmable Gate Array,
2025
University of South Alabama
Security Vulnerabilities Of A Field Programmable Gate Array, Kylie Arnett
Shelby Hall Graduate Research Forum Posters
The primary goal of this research is to understand and exploit the security vulnerabilities of a Field Programmable Gate Array (FPGA), at the bitstream level. This paper is working to successfully show that an FPGA can be altered via the bitstream file, and a Trojan can be inserted into the device. Once a Trojan is successfully inserted into the FPGA and activated through a certain input value, a Siamese Neural Network (SNN) will be used to test the effectiveness of Trojan detection. Followed by recording the successful flag rate to detect Trojans, which will be averaged to determine the accuracy …
Establishing A Framework For Evaluating Machine Learning Performance And Security Across Computational Ecosystems,
2025
University of South Alabama
Establishing A Framework For Evaluating Machine Learning Performance And Security Across Computational Ecosystems, Krista Stacey, Todd R. Andel
Shelby Hall Graduate Research Forum Posters
The rapid evolution of computational ecosystems—ranging from embedded systems and cloud platforms to hybrid and quantum architectures—has introduced new challenges in deploying machine learning (ML) applications. While cloud computing offers scalability, it comes with increased latency and security risks, whereas edge computing, such as FPGA-based systems, provides real-time processing with constrained resources. Hybrid and quantum ecosystems further complicate decision-making, requiring careful trade-offs between performance and security. This research seeks to establish a framework for evaluating ML performance and security risks across these ecosystems, forming the foundation of the Computational Performance And Security System (COMPASS) decision-support tool. The study will systematically …
Development Of An Algorithm To Identify The Presence Of Luks-Encrypted Volumes On A Forensic Image Of A Drive,
2025
University of South Alabama
Development Of An Algorithm To Identify The Presence Of Luks-Encrypted Volumes On A Forensic Image Of A Drive, Nicholas Flynn, Michael Black
Shelby Hall Graduate Research Forum Posters
Current forensic tools struggle to effectively detect encrypted storage media. In recent years, there have been significant advancements, but a noticeable gap remains when it comes to identifying encrypted volumes from metadata alone. The goal of this research is to develop a novel algorithm that will identify the presence of volumes on a disk image which have been encrypted with the Linux Unified Key Setup (LUKS) encryption algorithm, in an effort to aid digital forensics investigations. Bad actors often use encryption as an anti-forensics tool to pose significant challenges to forensic investigators, especially when it is done within sections of …
False Narratives, Real Consequences,
2025
University of South Alabama
False Narratives, Real Consequences, Russell W. Cantrell, Matt Campbell
Shelby Hall Graduate Research Forum Posters
Social media is an increasingly significant tool in modern cyber warfare, capable of rapidly shaping public opinion. The swift dissemination of information complicates efforts to distinguish fact from fiction [1]. During public health crises, healthcare professionals use these platforms to share updates, yet their credible content must contend with false or deliberately misleading narratives [2]. This environment creates an opportunity for cyberattacks through social media influence campaigns [3]. While disinformation's role in political interference has been widely studied, its potential to destabilize healthcare remains largely unexplored. Prior research primarily focuses on how vaccine misinformation affects the general public [4]. This …
Detecting Sensor Data Manipulation,
2025
University of South Alabama
Detecting Sensor Data Manipulation, Ricky Green, Michael Black
Shelby Hall Graduate Research Forum Posters
The integration of Information Technology (IT) and Operational Technology (OT) have made OT devices vulnerable to threats that have been successfully exploited with devastating results. Many modern techniques for hardening and securing enterprise IT systems are either incompatible with OT components in an Industrial Control System (ICS), reduce the efficiency of processes, or are prohibitively expensive to implement. Research in the area of ICS security focuses on a top-down approach, such as intrusion prevention by securing the perimeter of the network and hardening computer systems. This approach is useful in business IT systems, but full compatibility with OT components in …
Using Machine Learning Models To Improve The Cyber Physical Security Of Drones,
2025
University of South Alabama
Using Machine Learning Models To Improve The Cyber Physical Security Of Drones, Sean Lee, Aviv Segev
Shelby Hall Graduate Research Forum Posters
This research proposes a new manner of implementing machine learning models such that, when applied on a drone, it will be able to accurately identify and maintain the authenticity of the entity sending the control data to the drone. To begin with, the drone will, for a pre-determined amount of signals received per unit time, determine the average signal strength (RSSI) of them and use that average to determine the approximate distance between the drone and the source of those signals. This single data point will be fed into a custom implementation of the SCluStream algorithm (a real-time clustering machine …
Provable Security In Idealised Models,
2025
Indian Statistical Institute
Provable Security In Idealised Models, Chandranan Dhar
Doctoral Theses
This thesis is a compilation of provable security analyses of various cryptographic constructions in idealised models. The first construction examined is the ABR hash. We revisit the existing proof of the ABR hash in the random oracle model and identify significant errors in the proof. Although we are unable to correct the original proof, we establish the security of the ABR tree of height 3 from scratch, addressing the first non-trivial case. As our second contribution, we conduct a tight and comprehensive security analysis of the Ascon AEAD mode in the random permutation model. We show that the efficiency of …
Modeling And Evaluation Of False Data Injection Attacks (Fdia) In Der Inverters,
2025
University of North Dakota
Modeling And Evaluation Of False Data Injection Attacks (Fdia) In Der Inverters, Tanzim Jim Hassan, Akshay Ram Ramchandra, Farishta Rahman, Prakash Ranganathan
Graduate Research Achievement Day Posters
This poster develops and evaluates false data injection attack (FDIA) models to enhance the cybersecurity of distributed energy resources (DER) solar inverters using real-time frequency data from two Fronius single-phase inverters. Fifteen datasets with unique attack patterns were developed and analyzed using machine learning models, where the most challenging anomaly (V3) had F1 scores between 0.425 and 0.76, while the most detectable (V1) achieved up to 0.895. These findings contribute to improving anomaly detection mechanisms for securing distributed energy resources (DER) and ensuring grid stability.
Improving Rupture Status Prediction For Intracranial Aneurysms Using Wall Shear Stress Informatics,
2025
Michigan Technological University
Improving Rupture Status Prediction For Intracranial Aneurysms Using Wall Shear Stress Informatics, Jingfeng Jiang, Mostafa Rezaeitaleshmahalleh, Jinshan Tang, Joseph Gemmette, Aditya Pandey
Michigan Tech Publications
BACKGROUND: Wall shear stress (WSS) plays a crucial role in the natural history of intracranial aneurysms (IA). However, spatial variations among WSS have rarely been utilized to correlate with IAs' natural history. This study aims to establish the feasibility of using spatial patterns of WSS data to predict IAs' rupture status (i.e., ruptured versus unruptured). METHODS: "Patient-specific" computational fluid dynamics (CFD) simulations were performed for 112 IAs; each IA's rupture status was known from medical records. Recall that CFD-simulated hemodynamics data (wall shear stress and its derivatives) are located on unstructured meshes. Hence, we mapped WSS data from an unstructured …
"It's Definitely New And Different...It's Really Engaging": Understanding The Power Of Storytelling Towards Secure Password Creation,
2025
Utah State University
"It's Definitely New And Different...It's Really Engaging": Understanding The Power Of Storytelling Towards Secure Password Creation, Rizu Paudel, Mahdi Nasrullah Al-Ameen
Computer Science Student Research
There is a dearth in existing literature to attain systematic understanding of leveraging digital storytelling in security designs. As we begin to address this gap, we focused on user authentication where the existing password composition policies and password meters often fail to help users around creating a strong and memorable password. To this end, we conducted a lab study with 19 participants, where we updated our initial designs in an iterative manner based on their feedback. We then conducted a between-subject online study with 104 participants over Amazon Mechanical Turk to evaluate our designs. We found that all of our …
Design And Implementation Of Secured Hybrid Gateway Node For Securing Iot - Enabled Distribution Automation,
2025
Department of Computer Science and Engineering, University of Dar es Salaam, Dar es Salaam, Tanzania
Design And Implementation Of Secured Hybrid Gateway Node For Securing Iot - Enabled Distribution Automation, Ally Bitebo
Tanzania Journal of Engineering and Technology (TJET)
The integration of smart grid and Internet of Things (IoT) technologies plays a crucial role in enhancing the quality of services provided by traditional electrical grids. This combination has enabled the introduction of new services, such as demand response, automatic meter reading, and IoT-enabled Distribution Automation (IoT-DA), which incorporates sensors, actuators, intelligent electrical devices (IEDs), and information and communication technologies to monitor and control the grid. However, this integration also introduces network security risks, including Denial of Service (DoS) attacks, false data injection, and masquerading attacks, such as system node impersonation that can transmit incorrect readings, trigger false alarms, and …
Scalable And Ethical Insider Threat Detection Through Data Synthesis And Analysis By Llms,
2025
Dakota State University
Scalable And Ethical Insider Threat Detection Through Data Synthesis And Analysis By Llms, Haywood Gelman, John Hastings
Research & Publications
Insider threats wield an outsized influence on organizations, disproportionate to their small numbers. This is due to the internal access insiders have to systems, information, and infrastructure. Signals for such risks may be found in anonymous submissions to public web-based job search site reviews. This research studies the potential for large language models (LLMs) to analyze and detect insider threat sentiment within job site reviews. Addressing ethical data collection concerns, this research utilizes synthetic data generation using LLMs alongside existing job review datasets. A comparative analysis of sentiment scores generated by LLMs is benchmarked against expert human scoring. Findings reveal …
Comparing Unidirectional, Bidirectional, And Word2vec Models For Discovering Vulnerabilities In Compiled Lifted Code,
2025
Dakota State University
Comparing Unidirectional, Bidirectional, And Word2vec Models For Discovering Vulnerabilities In Compiled Lifted Code, Gary Mccully, John Hastings, Shengjie Xu, Adam Fortier
Research & Publications
Ransomware and other forms of malware cause significant financial and operational damage to organizations by exploiting long-standing and often difficult-to-detect software vulnerabilities. To detect vulnerabilities such as buffer overflows in compiled code, this research investigates the application of unidirectional transformer-based embeddings, specifically GPT-2. Using a dataset of LLVM functions, we trained a GPT-2 model to generate embeddings, which were subsequently used to build LSTM neural networks to differentiate between vulnerable and non-vulnerable code. Our study reveals that embeddings from the GPT-2 model significantly outperform those from bidirectional models of BERT and RoBERTa, achieving an accuracy of 92.5\% and an F1-score …
Computing In The Commonwealth: Specialized Education In Computer Science And Information Technology For High School Students In Virginia – An Environmental Scan,
2025
Virginia Commonwealth University
Computing In The Commonwealth: Specialized Education In Computer Science And Information Technology For High School Students In Virginia – An Environmental Scan, Amy Corning, Jonathan D. Becker, Jon Graham, James Carrigan, Keisha Tennessee
ICRE Publications
Over the past two decades, Virginia has invested substantially in STEM education, in part through specialized programs focused on computer science and information technology (CS/IT). This study represents the first effort to identify Virginia’s specialized secondary CS/IT programs and examine them collectively. Findings from the statewide environmental scan indicate that the programs are delivered through a wide variety of institutional structures, including Governor’s STEM Academies, Governor’s Schools, specialty centers, and academies, but most often through Career and Technical Education (CTE) centers. Programs tend to be concentrated in metropolitan areas, and some rural divisions may not be served. The programs provide …
A Survey-Based Quantitative Analysis Of Stress Factors And Their Impacts Among Cybersecurity Professionals,
2025
Dakota State University
A Survey-Based Quantitative Analysis Of Stress Factors And Their Impacts Among Cybersecurity Professionals, Sunil Arora, John D. Hastings
Research & Publications
This study investigates the prevalence and underlying causes of work-related stress and burnout among cybersecurity professionals using a quantitative survey approach guided by the Job Demands-Resources model. Analysis of responses from 50 cybersecurity practitioners reveals an alarming reality: 44% report experiencing severe work-related stress and burnout, while an additional 28% are uncertain about their condition. The demanding nature of cybersecurity roles, unrealistic expectations, and unsupportive organizational cultures emerge as primary factors fueling this crisis. Notably, 66% of respondents perceive cybersecurity jobs as more stressful than other IT positions, with 84% facing additional challenges due to the pandemic and recent high-profile …
