Computer Engineering Commons^™

Computer Security Lab Experiment, Orit D. Gruber, Herbert Schanker

Open Educational Resources

This is a basic experiment for all students of all majors to explore Computer Security. Each instruction included in this experiment is conducted online via a Web Browser; Firefox or Chrome is recommended. Software does not need to be downloaded nor installed. The step by step instructions in this experiment include interactive questions and observations which are then included in the (student's) final report.

Toward Intuitive 3d Interactions In Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach, Trudi Di Qi, Franceli L. Cibrian, Meghna Raswan, Tyler Kay, Hector M. Camarillo-Abad, Yuxin Wen

Engineering Faculty Articles and Research

Dual-hand gesture recognition is crucial for intuitive 3D interactions in virtual reality (VR), allowing the user to interact with virtual objects naturally through gestures using both handheld controllers. While deep learning and sensor-based technology have proven effective in recognizing single-hand gestures for 3D interactions, research on dual-hand gesture recognition for VR interactions is still underexplored. In this work, we introduce CWT-CNN-TCN, a novel deep learning model that combines a 2D Convolution Neural Network (CNN) with Continuous Wavelet Transformation (CWT) and a Temporal Convolution Network (TCN). This model can simultaneously extract features from the time-frequency domain and capture long-term dependencies using …

Path Planning And Cyber-Physical System Integration In Unmanned Aerial Vehicles For Wireless Communication, Mohamad Hani Sulieman

Dissertations - ALL

Unmanned Aerial Vehicles (UAVs) have become indispensable in a variety of fields, including surveillance, emergency response, packet delivery, and data collection for the Internet of Things (IoT). These systems are also critical in enhancing connectivity within cellular networks. This dissertation focuses on improving UAV operational efficiency and security by advancing trajectory optimization techniques, enhancing trajectory design through antenna radiation patterns, and increasing resilience against cyber-physical attacks, particularly GPS sensor faults. The first part of the study addresses UAV trajectory optimization in wireless communications, highlighting the significance of trajectory planning in improving the efficiency and reliability of UAV operations. Effective trajectory …

A Trustzone-Based Framework To Secure Mobile Financial Transactions And Provide End-To-End Protection For Qr-Code Payments And Credit Card Information, Ammar Salman Salman

Dissertations - ALL

In this work we have developed multiple solutions for financial transactions that can be coordinated to provide high level of security and data integrity, while providing all services with minimum changes in infrastructures, and maximum flexibility. The solutions are novel in many aspects and the generalization is a clear feature. The TrustZone hardware is an important component to ensure high security and protection. The system can function smoothly on any type of operating systems and works with any platform of services in the market. The tested case study is built on the Android system and the ARM TrustZone hardware. This …

Beyond The Horizon: Exploring Anomaly Detection Potentials With Federated Learning And Hybrid Transformers In Spacecraft Telemetry, Juan Rodriguez

Computer Science and Engineering Theses and Dissertations

Telemetry sensors play a crucial role in spacecraft operations, providing essential data on efficiency, sustainability, and safety. However, identifying irregularities in telemetry data can be a time-consuming process that risks the success of missions. With the rise of CubeSats and smallsats, telemetry data has become more abundant, but concerns about privacy and scalability have resulted in untapped data potential. To address these issues, we propose a new approach to anomaly detection that utilizes machine learning models at data sources. These models solely transmit weights to a centralized server for aggregation, resulting in improved dataset performance with a single global model. …

Development Of Hybrid Multi Criteria Decision Making Techniques For Efficient Cloud Service Selection, Obulaporam Gireesha

Theses and Dissertations

During the past few decades, cloud computing became a primary driver for the next generation of digital technology due to the increase in organizational performance and profitability based on a ‘pay-as-you-use’ fashion at anytime and anywhere across the globe. Cloud computing enables various enterprises to access pooled resources (like storage, network bandwidth, software applications, processing power, etc.) over the Internet with minimal Information Technology (IT) infrastructure and capital expenditure.

Indeed, the enormous popularity of cloud computing in both academia & industry over the decade has resulted in a wide range of similar cloud services offered by numerous service providers. Even …

Neuro-Symbolic Commonsense Reasoning With Resistance To Data Poisoning: A First-Order Logic And Sub-Symbolic Embeddings Framework, Bryce Shurts, King-Ip Lin

Computer Science and Engineering Theses and Dissertations

Commonsense reasoning has long presented a hurdle between conversational agents and their ability to naturally engage with humans in conversation, as the infinitely dimensional nature of a dialogue’s topics presents a significant reasoning challenge in the study of Natural Language Understanding (NLU). Such a system must conceivably be able to act as a generalizable system for evaluating and reasoning about commonsense statements, problems, and queries: in this way, the agent can attempt to quantify the reasonability of a given input. We attempt to address this through the integration of an explainable neuro-symbolic system that leverages Logical Tensor Networks (LTNs) and …

Securing The Skies: Safety-Constrained Decentralized Multi-Uav Coordination With Deep Reinforcement Learning, Jean-Elie Pierre

Electrical and Computer Engineering ETDs

In the dynamic landscape of autonomous aerial systems, the integration of uncrewed aerial vehicles (UAVs) has sparked a paradigm shift, offering unprecedented opportunities and challenges in collaborative decision-making and navigation. This thesis explores the application of multi-agent reinforcement learning (MARL) for the planning and coordination of UAVs in complex environments.

The first part of this thesis provides an introduction to single-agent reinforcement learning and MARL. We provide examples of the use of MARL for countering uncrewed aerial systems (C-UAS). We formulate the Counter-UAS problem as a multiagent partially observable Markov decision process (MAPOMDP), and we propose Multi-AGent partial observable deep …

Latent Auto-Recursive Composition Engine, Yenkai Huang

Dartmouth College Master’s Theses

This thesis investigates the shifting boundaries of art in the era of Generative AI, critically examining the essence of art and the legitimacy of AI-generated works. Despite significant advancements in the quality and accessibility of art through generative AI, such creations frequently encounter skepticism regarding their status as authentic art. To address this skepticism, the study explores the role of creative agency in various generative AI workflows and introduces an "artist-in-the-loop" system tailored for image generation models like Stable Diffusion. This system aims to deepen the artist's engagement and understanding of the creative process. Additionally, a novel tool, the Latent …

Generalized Model To Enable Zero-Shot Imitation Learning For Versatile Robots, Yongshuai Wu

Master's Theses

The rapid advancement in Deep Learning (DL), especially in Reinforcement Learning (RL) and Imitation Learning (IL), has positioned it as a promising approach for a multitude of autonomous robotic systems. However, the current methodologies are predominantly constrained to singular setups, necessitating substantial data and extensive training periods. Moreover, these methods have exhibited suboptimal performance in tasks requiring long-horizontal maneuvers, such as Radio Frequency Identification (RFID) inventory, where a robot requires thousands of steps to complete.

In this thesis, we address the aforementioned challenges by presenting the Cross-modal Reasoning Model (CMRM), a novel zero-shot Imitation Learning policy, to tackle long-horizontal robotic …

Brain Computer Interface-Based Drone Control Using Gyroscopic Data From Head Movements, Ikaia Cacha Melton

Honors College Theses

This research explores the potential of using gyroscopic data from a person’s head movement to control a DJI Tello quadcopter via a Brain-Computer Interface (BCI). In this study, over 100 gyroscopic recordings capturing the X, Y and Z columns (formally known as GyroX, GyroY, GyroZ) between 4 volunteers with the Emotiv Epoc X headset were collected. The Emotiv Epoc X data captured (left, right, still, and forward) head movements of each participant associated with the DJI Tello quadcopter navigation. The data underwent thorough processing and analysis, revealing distinctive patterns in charts using Microsoft Excel. A Python condition algorithm was then …

Whispers Of Ai: Unveiling The Paradox Of Gpt Titans, Faria R. Promi, Qing Qing Zhuo

Publications and Research

The advent of large-scale language models, such as GPT, has sparked a revolution in artificial intelligence, enabling computers to comprehend and generate human-like text with remarkable ease. These models can write articles, answer questions, and even engage in conversations that mimic human speech. While their abilities are impressive, concerns about their societal impact abound. This research project dives deep into exploring the multifaceted aspects of these AI titans. We will investigate the positive aspects of big talking AI models through a thorough examination of existing literature, real-world examples, such as their potential to enhance education, entertainment, and customer service experiences. …

Practical Static Binary Instrumentation Attacks Against Binary Stylometry, Justin Lee Carpenter

Masters Theses, 2020-current

Binary stylometry aims to find the features in a binary computer program and use them to identify the developers of the corresponding source code. Despite the noises in the code compilation process from the compiler, assembler, linker, and library functions, two existing studies based on machine learning for binary stylometry have reported high success rates (Alrabaee, Shirani, Wang, Debbabi, and Hanna 2018; Caliskan, Yamaguchi, Dauber, Harang, Rieck, Greenstadt, and Narayanan 2018). In this thesis, we first observe that both existing studies are based on a largely benign security model and assume that the binaries used in testing and prediction are …

Flexible Strain Gauge Sensors As Real-Time Stretch Receptors For Use In Biomimetic Bpa Muscle Applications, Rochelle Jubert

Student Research Symposium

This work presents a novel approach to real-time length sensing for biomimetic Braided Pneumatic Actuators (BPAs) as artificial muscles in soft robotics applications. The use of artificial muscles enables the development of more interesting robotic designs that no longer depend on single rotation joints controlled by motors. Developing robots with these capabilities, however, produces more complexities in control and sensing. Joint encoders, the mainstay of robotic feedback, can no longer be used, so new methods of sensing are needed to get feedback on muscle behavior to implement intelligent controls. To address this need, flexible strain gauge sensors from Portland company, …

Chiroptical Second-Harmonic Tyndall Scattering From Silicon Nanohelices, Ben J. Olohan, Emilija Petronijevic, Ufuk Kilic, Shawn Wimer, Matthew Hilfiker, Mathias Schubert, Christos Argyropoulos, Eva Schubert, Samuel R. Clowes, G. Dan Pantoş, David L. Andrews, Ventsislav K. Valev

Department of Electrical and Computer Engineering: Faculty Publications

Chirality is omnipresent in the living world. As biomimetic nanotechnology and self-assembly advance, they too need chirality. Accordingly, there is a pressing need to develop general methods to characterize chiral building blocks at the nanoscale in liquids such as water-the medium of life. Here, we demonstrate the chiroptical second-harmonic Tyndall scattering effect. The effect was observed in Si nanohelices, an example of a high-refractive-index dielectric nanomaterial. For three wavelengths of illumination, we observe a clear difference in the second-harmonic scattered light that depends on the chirality of the nanohelices and the handedness of circularly polarized light. Importantly, we provide a …

Evaluating The Effect Of Noise On Secure Quantum Networks, Karthick Anbalagan

Master's Theses

This thesis focuses on examining the resilience of secure quantum networks to environmental noise. Specifically, we evaluate the effectiveness of two well-known quantum key distribution (QKD) protocols: the Coherent One-Way (COW) protocol and Kak’s Three-Stage protocol (Kak06). The thesis systematically evaluates these protocols in terms of their efficiency, operational feasibility, and resistance to noise, thereby contributing to the progress of secure quantum communications. Using simulations, this study evaluates the protocols in realistic scenarios that include factors such as noise and decoherence. The results illustrate each protocol’s relative benefits and limitations, highlighting the three-stage protocol’s superior security characteristics, resistance to interference, …

Robustness Of Trajectory Prediction Neural Network Models, Guocheng He

McKelvey School of Engineering Graduate Student Theses & Dissertations

The application of autonomous vehicles in real life relies on trajectory prediction models based on perception and observation of the surrounding scene. The deep neural network model has been widely proven to provide relatively stable and excellent performance in various scenarios. Many formal approaches are used as verification of the prediction results of DNN models, where Conformal Prediction is one which can provide statistical safety guarantee region for DNN models. However, so far, no research has shown that conformal prediction possesses satisfactory robustness in dealing with purposed adversarial attacks. In this paper, we propose an adversarial attack approach against trajectory …

Machine Learning For Intrusion Detection Into Unmanned Aerial System 6g Networks, Faisal Alrefaei

Doctoral Dissertations and Master's Theses

Progress in the development of wireless network technology has played a crucial role in the evolution of societies and provided remarkable services over the past decades. It remotely offers the ability to execute critical missions and effective services that meet the user's needs. This advanced technology integrates cyber and physical layers to form cyber-physical systems (CPS), such as the Unmanned Aerial System (UAS), which consists of an Unmanned Aerial Vehicle (UAV), ground network infrastructure, communication link, etc. Furthermore, it plays a crucial role in connecting objects to create and develop the Internet of Things (IoT) technology. Therefore, the emergence of …

Improving Ethics Surrounding Collegiate-Level Hacking Education: Recommended Implementation Plan & Affiliation With Peer-Led Initiatives, Shannon Morgan, Dr. Sanjay Goel

Military Cyber Affairs

Cybersecurity has become a pertinent concern, as novel technological innovations create opportunities for threat actors to exfiltrate sensitive data. To meet the demand for professionals in the workforce, universities have ramped up their academic offerings to provide a broad range of cyber-related programs (e.g., cybersecurity, informatics, information technology, digital forensics, computer science, & engineering). As the tactics, techniques, and procedures (TTPs) of hackers evolve, the knowledge and skillset required to be an effective cybersecurity professional have escalated accordingly. Therefore, it is critical to train cyber students both technically and theoretically to actively combat cyber criminals and protect the confidentiality, integrity, …

Using Digital Twins To Protect Biomanufacturing From Cyberattacks, Brenden Fraser-Hevlin, Alec W. Schuler, B. Arda Gozen, Bernard J. Van Wie

Military Cyber Affairs

Understanding of the intersection of cyber vulnerabilities and bioprocess regulation is critical with the rise of artificial intelligence and machine learning in manufacturing. We detail a case study in which we model cyberattacks on network-mediated signals from a novel bioreactor, where it is important to control medium feed rates to maintain cell proliferation. We use a digital twin counterpart reactor to compare glucose and oxygen sensor signals from the bioreactor to predictions from a kinetic growth model, allowing discernment of faulty sensors from hacked signals. Our results demonstrate a successful biomanufacturing cyberattack detection system based on fundamental process control principles.

Characterizing Advanced Persistent Threats Through The Lens Of Cyber Attack Flows, Logan Zeien, Caleb Chang, Ltc Ekzhin Ear, Dr. Shouhuai Xu

Military Cyber Affairs

Effective cyber defense must build upon a deep understanding of real-world cyberattacks to guide the design and deployment of appropriate defensive measures against current and future attacks. In this abridged paper (of which the full paper is available online), we present important concepts for understanding Advanced Persistent Threats (APTs), our methodology to characterize APTs through the lens of attack flows, and a detailed case study of APT28 that demonstrates our method’s viability to draw useful insights. This paper makes three technical contributions. First, we propose a novel method of constructing attack flows to describe APTs. This abstraction allows technical audiences, …

Generative Machine Learning For Cyber Security, James Halvorsen, Dr. Assefaw Gebremedhin

Military Cyber Affairs

Automated approaches to cyber security based on machine learning will be necessary to combat the next generation of cyber-attacks. Current machine learning tools, however, are difficult to develop and deploy due to issues such as data availability and high false positive rates. Generative models can help solve data-related issues by creating high quality synthetic data for training and testing. Furthermore, some generative architectures are multipurpose, and when used for tasks such as intrusion detection, can outperform existing classifier models. This paper demonstrates how the future of cyber security stands to benefit from continued research on generative models.

Machine Learning Security For Tactical Operations, Dr. Denaria Fields, Shakiya A. Friend, Andrew Hermansen, Dr. Tugba Erpek, Dr. Yalin E. Sagduyu

Military Cyber Affairs

Deep learning finds rich applications in the tactical domain by learning from diverse data sources and performing difficult tasks to support mission-critical applications. However, deep learning models are susceptible to various attacks and exploits. In this paper, we first discuss application areas of deep learning in the tactical domain. Next, we present adversarial machine learning as an emerging attack vector and discuss the impact of adversarial attacks on the deep learning performance. Finally, we discuss potential defense methods that can be applied against these attacks.

Securing The Void: Assessing The Dynamic Threat Landscape Of Space, Brianna Bace, Dr. Unal Tatar

Military Cyber Affairs

Outer space is a strategic and multifaceted domain that is a crossroads for political, military, and economic interests. From a defense perspective, the U.S. military and intelligence community rely heavily on satellite networks to meet national security objectives and execute military operations and intelligence gathering. This paper examines the evolving threat landscape of the space sector, encompassing natural and man-made perils, emphasizing the rise of cyber threats and the complexity introduced by dual-use technology and commercialization. It also explores the implications for security and resilience, advocating for collaborative efforts among international organizations, governments, and industry to safeguard the space sector.

Commercial Enablers Of China’S Cyber-Intelligence And Information Operations, Ethan Mansour, Victor Mukora

Military Cyber Affairs

In a globally commercialized information environment, China uses evolving commercial enabler networks to position and project its goals. They do this through cyber, intelligence, and information operations. This paper breaks down the types of commercial enablers and how they are used operationally. It will also address the CCP's strategy to gather and influence foreign and domestic populations throughout cyberspace. Finally, we conclude with recommendations for mitigating the influence of PRC commercial enablers.

Analysis And Evaluation Of Different Transformer Architectures For The Protein Sequence Representation And The Corresponding Hypothetical Applications, Mary Mao

Theses and Dissertations

This project is to investigate and assess several Transformer topologies for the modeling of protein sequences and their corresponding uses. Each major model for the protein sequence representations is inspected with its mathematical theory and analyzed for the different performance of the models with various validation repositories.

Rgb Root Matriz Color Dance, Danielle E. Gauthier

Theses and Dissertations

RGB Root Matriz Color Dance (Color Dance) is an immersive, interactive experience that combines poetic phrases and color filters to create a womb-like environment. Designed by Danielle Gauthier, this artistic piece uses a webcam to respond to users’ movements in real time, allowing them to confront and express their emotions through metaphor and dance. Color Dance creates a unique platform for self-discovery and empowerment by fostering a connection between the body and discomforting emotions.

Simulating Information And Communication Applications In Employee Interaction Network Models, Matthew Kanter

Departmental Honors & Graduate Capstone Projects

Information and communication technology (ICT) use has been identified throughout its development and evolution with the Internet boom as a net positive tool for most employees and organizations in the working world. Only recently have studies regarding employees’ well-being begun to come to the forefront of research regarding these rapidly evolving technologies, however these are important issues to discuss in the context of work-life boundary management, emotional exhaustion, overwhelming stress levels, and moral disengagement among other employee well-being dimensions. To explore how employees’ well being might be influenced by ICT use, this study conducted a quantitative survey and analyzed a …

Analyzing Information Cascades Through Machine Learning And Data Analytics, Betul Agirman

Honors Scholar Theses

In today's digital age, social media platforms have become pivotal in influencing public opinion and behavior, with information spreading being both beneficial and detrimental. This rapid spread is typically called an information cascade, and they are important in further understanding social influence, managing misinformation, and even predicting potential trends of public responses. With social media, people are connected so easily to one another like a network, wherein it becomes possible for them to influence each other’s behavior and decisions. Utilizing a dataset from Weibo that spans critical periods of the COVID-19 outbreak, this study integrates machine learning and data analytics …

Thematic Synthesis: Rethinking Generative Music With Compositional Understanding In Game And Software Development, Jasper Moore Tucker

Dartmouth College Master’s Theses

Generative music, first introduced by composers like Brian Eno and David Cope in the mid-to-late 20th century, has evolved through many stages, with diverse applications across music and technology companies as well as the game industry. However, despite this widespread interest, there remains a notable lack of the foundational understanding of composition and individual expressiveness in current generative systems that was apparent in the work of early composers. This paper advocates for a shift towards prioritizing compositional thought in system design to foster greater diversity and innovation within generative music. To demonstrate this approach, a novel system synthesizing two distinct …