- ItemOpen AccessCPS security testbed: requirement analysis, prototype design and protection framework(Colorado State University. Libraries, 2023) Talukder, Md Rakibul Hasan, author; Ray, Indrajit, advisor; Malaiya, Yashwant, committee member; Vijayasarathy, Leo, committee memberTestbeds are a practical way to perform security exercises on cyber physical systems (CPS) to understand vulnerabilities and the progression/impact of cyber-attacks. However, it is challenging to replicate a large CPS, such as nuclear power plant or an electrical power grid, within the confines of a laboratory that would allow security experiments to be carried out. Thus, software-based simulations are getting increasingly popular as opposed to hardware-in-the-loop based simulations for CPS that form a critical infrastructure. Unfortunately, a software-based CPS testbed oriented towards security-centric experiments requires a careful re-examination of requirements and architectural design different from a CPS testbed for non-security related experiments. On a security-focused testbed there is a need to run real attack scripts for red-teaming/blue-teaming exercises, which are, in the strictest sense of the term, malicious in nature. Thus, there is a need to protect the testbed itself from these attack experiments that have the potential to go awry. The overall effect of an exploit on the whole system or vulnerabilities at communication channels needs to be particularly explored while building a simulator for a security-centric CPS. Besides, when multiple experiments are conducted on the same testbed, there is a need to maintain isolation among these experiments so that no experiment can accidentally or maliciously compromise others and affect the fidelity of those results. Specific security experiment-related supports are essential when designing such a testbed but integrating a software-based simulator within the testbed to provide necessary experiment support is challenging. In this thesis, we make three contributions. First, we present the design of an ideal testbed based on a set of requirements and supports that we have identified, focusing specifically on security experiment as the primary use case. Next, following these requirements analysis, we integrate a software-based simulator (Generic Pressurized Water Reactor) into a testbed design by modifying the implementation architecture to allow the execution of attack experiments on different networking architectures and protocols. Finally, we describe a novel security architecture and framework to ensure the protection of security-related experiments on a CPS testbed.
- ItemOpen AccessApplication of the neural data transformer to non-autonomous dynamical systems(Colorado State University. Libraries, 2023) Mifsud, Domenick M., author; Ortega, Francisco R., advisor; Anderson, Charles, advisor; Thomas, Micheal, committee member; Barreto, Armando, committee memberThe Neural Data Transformer (NDT) is a novel non-recurrent neural network designed to model neural population activity, offering faster inference times and the potential to advance real-time applications in neuroscience. In this study, we expand the applicability of the NDT to non-autonomous dynamical systems by investigating its performance on modeling data from the Chaotic Recurrent Neural Network (RNN) with delta pulse inputs. Through adjustments to the NDT architecture, we demonstrate its capability to accurately capture non-autonomous neural population dynamics, making it suitable for a broader range of Brain-Computer Inter-face (BCI) control applications. Additionally, we introduce a modification to the model that enables the extraction of interpretable inferred inputs, further enhancing the utility of the NDT as a powerful and versatile tool for real-time BCI applications.
- ItemOpen AccessIntentional microgesture recognition for extended human-computer interaction(Colorado State University. Libraries, 2023) Kandoi, Chirag, author; Blanchard, Nathaniel, advisor; Krishnaswamy, Nikhil, advisor; Soto, Hortensia, committee memberAs extended reality becomes more ubiquitous, people will more frequently interact with computer systems using gestures instead of peripheral devices. However, previous works have shown that using traditional gestures (pointing, swiping, etc.) in mid-air causes fatigue, rendering them largely unsuitable for long-term use. Some of the same researchers have promoted "microgestures"---smaller gestures requiring less gross motion---as a solution, but to date there is no dataset of intentional microgestures available to train computer vision algorithms for use in downstream interactions with computer systems such as agents deployed on XR headsets. As a step toward addressing this challenge, I present a novel video dataset of microgestures, classification results from a variety of ML models showcasing the feasibility (and difficulty) of detecting these fine-grained movements, and discuss the challenges in developing robust recognition of microgestures for human-computer interaction.
- ItemEmbargoCollaborating with artists to design additional multimodal and unimodal interaction techniques for three-dimensional drawing in virtual reality(Colorado State University. Libraries, 2023) Sullivan, Brian T., author; Ortega, Francisco, advisor; Ghosh, Sudipto, committee member; Tornatzky, Cyane, committee member; Barrera Machuca, Mayra, committee member; Batmaz, Anil Ufuk, committee memberAlthough drawing is an old and common mode of human creativity and expression, virtual reality (VR) has presented an opportunity for a novel form of drawing. Instead of representing three-dimensional objects with marks on a two-dimensional surface, VR permits people to create three-dimensional (3D) drawings in midair. It remains unknown, however, what would constitute an optimal interface for 3D drawing in VR. This thesis helps to answer this question by describing a co-design study conducted with artists to identify desired multimodal and unimodal interaction techniques to incorporate into user interfaces for 3D VR drawing. Numerous modalities and interaction techniques were proposed in this study, which can inform future research into interaction techniques for this developing medium.
- ItemEmbargoMachine learning and deep learning applications in neuroimaging for brain age prediction(Colorado State University. Libraries, 2023) Vafaei, Fereydoon, author; Anderson, Charles, advisor; Kirby, Michael, committee member; Blanchard, Nathaniel, committee member; Burzynska, Agnieszka, committee memberMachine Learning (ML) and Deep Learning (DL) are now considered as state-of-the-art assistive AI technologies that help neuroscientists, neurologists and medical professionals with early diagnosis of neurodegenerative diseases and cognitive decline as a consequence of unhealthy brain aging. Brain Age Prediction (BAP) is the process of estimating a person's biological age using Neuroimaging data, and the difference between the predicted age and the subject's chronological age, known as Delta, is regarded as a biomarker for healthy versus unhealthy brain aging. Accurate and efficient BAP is an important research topic, and hence ML/DL methods have been developed for this task. There are different modalities of Neuroimaging such as Magnetic Resonance Imaging (MRI) that have been used for BAP in the past. Diffusion Tensor Imaging (DTI) is an advanced quantitative Neuroimaging technology that gives insight into microstructure of White Matter tracts that connect different parts of the brain to function properly. DTI data is high-dimensional, and age-related microstructural changes in White Matter include non-linear patterns. In this study, we perform a series of analytical experiments using ML and DL methods to investigate the applicability of DTI data for BAP. We also investigate which Diffusivity Parameters, which are DTI metrics that reflect direction and magnitude of diffusion of water molecules in the brain, are relevant for BAP as a Supervised Learning task. Moreover, we propose, implement, and analyze a novel methodology that can detect age-related anomalies (high Deltas), and can overcome some of the major and fundamental limitations of the current supervised approach for BAP, such as "Chronological Age Label Inconsistency". Our proposed methodology, which combines Unsupervised Anomaly Detection (UAD) and supervised BAP, focuses on addressing a fundamental challenge in BAP which is how to interpret a model's error. Should a researcher interpret a model's error as an indication of unhealthy brain aging or the model's poor performance that should be eliminated? We argue that the underlying cause of this problem is the inconsistency of chronological age labels as the ground truth of the Supervised Learning task, which is the common basis of training ML/DL models. Our Unsupervised Learning methods and findings open a new possibility to detect irregularities and abnormalities in the aging brain using DTI scans, independent of inconsistent chronological age labels. The results of our proposed methodology show that combining label-independent UAD and supervised BAP provides a more reliable and methodical way for error analysis than the current supervised BAP approach when it is used in isolation. We also provide visualization and explanations on how our ML/DL methods make their decisions for BAP. Explainability and generalization of our ML/DL models are two important aspects of our study.