Other Computer Sciences Commons^™

Text Corpus Combined Method And Tools For Music Textual Analysis, Yuwei Lu

Shelby Hall Graduate Research Forum Posters

While there has been a great deal of research conducted on how to search images and video using text, there has been less focus on how to retrieve music using multiple facets such as mood and lyrical content. or its features This is due in part to the historical lack of available musical corpuses. A musical corpus is a specialized text corpus specifically designed to capture information about music. Recently, several musical corpora for music information retrieval have been built and made available. However, these corpora and the tools designed to exploit them are typically designed to facilitate only one …

Integrating Nonlinear Phase Space Analysis And Image-Based Representation For Network Intrusion Detection, Chakriya Suon

Shelby Hall Graduate Research Forum Posters

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 applies novel machine learning (ML)techniques to detect network intrusions effectively. Our primary focus is to extend prior research, which has used network flows that are processed by a nonlinear phase space algorithm (NLPSA). The NLSPA approach has proven extremely effective in detecting anomalous or malicious traffic patterns on representative data but requires extensive training time.

Our contribution integrates deep learning into the anomaly detection approach by creating image-based representations of the adjacency …

Cellebrite Reliability In Digital Forensics, Christina Huynh

Shelby Hall Graduate Research Forum Posters

Forensic tools like Cellebrite are commonly used in court to gather and interpret raw data for evidence. Cellebrite does not only collect data but creates and interprets the artifacts of data to create a scene of the process it has been through. With this, evidence can be influenced by software designs and not just the data on the mobile device. Courts and police use Cellebrite to gather evidence and reconstruct it to create an easily readable dataset. These tools lack reproducibility, transparency, integrity, and chain of evidence command. Cellebrite is often used in court and by police without further vetting …

Turbulence Prediction Using Non-Linear Phase Space Analysis, Jeremy Quijano

Shelby Hall Graduate Research Forum Posters

Our research presents a novel approach for turbulence prediction in computational fluid dynamics (CFD) simulations using a non-linear phase space analysis (NLPSA) and threshold algorithm. NLPSA has been utilized in medical applications to predict seizures, as well as in cybersecurity to detect malicious control and utilization of computing systems. NLPSA uses time-series data to learn the normal operating state of the system, then sets a threshold to predict when the system becomes abnormal. Turbulence prediction is similar, such that a fluid system changes from normal to abnormal. Turbulence prediction methods currently utilize machine learning tools, such as convolutional neural networks …

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, …

A Framework For Design Recovery, Eric Diep

Shelby Hall Graduate Research Forum Posters

Due to the increase in diverse chip production over the past decade, reverse engineering has become a difficult and daunting task. This research will create a guideline for methods to achieve design recovery of microchip logic. To accomplish this, we plan on using hardware tools such as laser delayering and microscopy imaging. We will be focusing on the DA14580 Dialog semiconductor, commonly implemented in tile trackers, to extract information for design recovery. This implementation technique is novel due to tools that have not been performed with this type of microchip. The purpose for this research is to create a framework …

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 …

Polyglot File Detection For Forensics, Chase Stevens, Michael Black

Shelby Hall Graduate Research Forum Posters

Polyglot files are problematic as payloads can be hidden inside them while simultaneously evading discovery by current forensic tools. Autopsy, one of the most used forensic tools in investigations, uses known signatures of file types (e.g., headers, trailers) to identify and recover files. Polyglot files are a unique case in which files are intentionally combined with other file types to make them appear benign while still containing malicious payloads. Polyglots are possible due to combinations being considered valid in both formats. Polyglot files inherently evade detection from signature-based forensic tools and malware scanners because the tools and malware scanners are …

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 …

Directing Sophisticated Cyberattacks On Public Water Infrastructure, Ayrton C. Purdy, Jordan Shropshire

Shelby Hall Graduate Research Forum Posters

In recent years there has been an increasing number of cyberattacks on public water generation and distribution systems. Advanced persistent attackers could usurp sensor and control systems to contaminate public drinking water. In order to conceal their malicious activity, they can manipulate sensor data flows to give the appearance of normal activity. The compromised sensors would report normal chemical levels even though unsafe water is entering the distribution system. In response, this research proposes a multi-sensor, cross-validation approach to anomaly detection. The proposed approach is designed to detect sophisticated cyberattacks which are not easily detectable using traditional cyber tools. The …

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, 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 …

Application Of Graph Neural Networks With Phase Space Graphs, Parker H. Cole, Ryan Benton, Ralf Riedel, David Bourrie

Shelby Hall Graduate Research Forum Posters

Non-linear phase-space analysis models data represented as a graph transitioning between states in the time domain. By studying data transitions, we can predict the time a particular behavior occurs and classify the events (states) in a system. For example, we could classify neurological sensor data to determine if a person is asleep (state), or predict the direction in which a stock will move (transitions) based on micro trade patterns.

Previous research has demonstrated success in phase-space graphs in classifying malware, detecting network intrusions, and predicting seizures. However, the solutions either require calculating global graph features as inputs to a classifier, …

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 …

Preserving Privacy In Senior Care At Home Monitoring Systems, Sam Russel, Ryan Benton, Amy Campbell, Scott Sittig

Shelby Hall Graduate Research Forum Posters

Many seniors prefer to live at home which necessitates research into the application of technologies to provide a safer environment with less caregiver resources. However, the application of home health care (HHC) monitoring for seniors is still in an evolutionary stage. Present HHC systems are produced by private companies with general regulatory guidelines lacking specific care of the elderly. As such, each company that produces such a system claims to have better safety, privacy, and security that their competitors. A pressing issues is devising and applying a general framework for the application of technologies that delivers safety while preserving privacy. …

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, 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 …

Topical Text Segmentation For Stream Of Consciousness Writing, Yuwei Lu, Ryan Benton

Shelby Hall Graduate Research Forum Posters

Stream of consciousness writing has a long history, including novelists James Joyce and Virginia Woolf. However, there has been little work done in automated and semi-automated analysis of such writing, which is the focus of this work. We plan to divide real streams of consciousness writing into distinct topical units and then capture different momentary meaningful topics from these units. By doing this, researchers and readers could gain a more nuanced understanding of the narrative structure and thematic elements. In addition, it would also support applications in fields like psychology and linguistics, where understanding thought processes and narrative structures is …

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 …