OS and Networks Commons^™

Leveraging P4 Programmable Switches For Resilient Operation And Design Of Phasor Measurement Unit Networks, Eva Casto

Electrical Engineering and Computer Science Undergraduate Honors Theses

The power grid utilizes a device called the phasor measurement unit (PMU), allowing power system administrators to remotely monitor and manage the state of the grid in Wide Area Monitoring Systems (WAMS). The advantages of PMUs – such as fine-grained, time-synchronized measurements and efficient, decentralized monitoring – are what make them key devices in the power grid. However, PMU technology also comes with new threats of the digital age, like malfunctions and cyberattacks, which can result in missing and faulty measurements that compromise power grid observability. P4 programmable networks can be used to detect faulty PMU data in a decentralized, …

An In-Network Approach For Pmu Missing Data Recovery With Data Plane Programmability, Jack Norris

Computer Science and Computer Engineering Undergraduate Honors Theses

Phasor measurement unit (PMU) systems often experience unavoidable missing and erroneous measurements, which undermine power system observability and operational effectiveness. Traditional solutions for recovering missing PMU data employ a centralized approach at the control center, resulting in lengthy recovery times due to data transmission and aggregation. In this work, we leverage P4-based programmable networks to expedite missing data recovery. Our approach utilizes the data plane programmability offered by P4 to present an in-network solution for PMU data recovery. We establish a data-plane pipeline on P4 switches, featuring a customized PMU protocol parser, a missing data detection module, and an auto-regressive …

Deep Learning For Photovoltaic Characterization, Adrian Manuel De Luis Garcia

Graduate Theses and Dissertations

This thesis introduces a novel approach to Photovoltaic (PV) installation segmentation by proposing a new architecture to understand and identify PV modules from overhead imagery. Pivotal to this concept is the creation of a new Transformer-based network, S3Former, which focuses on small object characterization and modelling intra- and inter- object differentiation inside an image. Accurate mapping of PV installations is pivotal for understanding their adoption and guiding energy policy decisions. Drawing insights from current Deep Learning methodologies for image segmentation and building upon State-of-the-Art (SOTA) techniques in solar cell mapping, this work puts forth S3Former with the following enhancements: 1. …

Linux Malware Obfuscation, Brian Roden

Computer Science and Computer Engineering Undergraduate Honors Theses

Many forms of malicious software use techniques and tools that make it harder for their functionality to be parsed, both by antivirus software and reverse-engineering methods. Historically, the vast majority of malware has been written for the Windows operating system due to its large user base. As such, most efforts made for malware detection and analysis have been performed on that platform. However, in recent years, we have seen an increase in malware targeting servers running Linux and other Unix-like operating systems resulting in more emphasis of malware research on these platforms. In this work, several obfuscation techniques for Linux …

An Automated Method To Enrich Consumer Health Vocabularies Using Glove Word Embeddings And An Auxiliary Lexical Resource, Mohammed Ibrahim, Susan Gauch, Omar Salman, Mohammed Alqahtani

Computer Science and Computer Engineering Faculty Publications and Presentations

Background

Clear language makes communication easier between any two parties. A layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical terminology which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa.

Objective

Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the …

Semi-Supervised Spatial-Temporal Feature Learning On Anomaly-Based Network Intrusion Detection, Huy Mai

Computer Science and Computer Engineering Undergraduate Honors Theses

Due to a rapid increase in network traffic, it is growing more imperative to have systems that detect attacks that are both known and unknown to networks. Anomaly-based detection methods utilize deep learning techniques, including semi-supervised learning, in order to effectively detect these attacks. Semi-supervision is advantageous as it doesn't fully depend on the labelling of network traffic data points, which may be a daunting task especially considering the amount of traffic data collected. Even though deep learning models such as the convolutional neural network have been integrated into a number of proposed network intrusion detection systems in recent years, …

Network-Based Detection And Prevention System Against Dns-Based Attacks, Yasir Faraj Mohammed

Graduate Theses and Dissertations

Individuals and organizations rely on the Internet as an essential environment for personal or business transactions. However, individuals and organizations have been primary targets for attacks that steal sensitive data. Adversaries can use different approaches to hide their activities inside the compromised network and communicate covertly between the malicious servers and the victims. The domain name system (DNS) protocol is one of these approaches that adversaries use to transfer stolen data outside the organization's network using various forms of DNS tunneling attacks. The main reason for targeting the DNS protocol is because DNS is available in almost every network, ignored, …

Low-Power And Reconfigurable Asynchronous Asic Design Implementing Recurrent Neural Networks, Spencer Nelson

Graduate Theses and Dissertations

Artificial intelligence (AI) has experienced a tremendous surge in recent years, resulting in high demand for a wide array of implementations of algorithms in the field. With the rise of Internet-of-Things devices, the need for artificial intelligence algorithms implemented in hardware with tight design restrictions has become even more prevalent. In terms of low power and area, ASIC implementations have the best case. However, these implementations suffer from high non-recurring engineering costs, long time-to-market, and a complete lack of flexibility, which significantly hurts their appeal in an environment where time-to-market is so critical. The time-to-market gap can be shortened through …

Prediction, Recommendation And Group Analytics Models In The Domain Of Mashup Services And Cyber-Argumentation Platform, Md Mahfuzer Rahman

Graduate Theses and Dissertations

Mashup application development is becoming a widespread software development practice due to its appeal for a shorter application development period. Application developers usually use web APIs from different sources to create a new streamlined service and provide various features to end-users. This kind of practice saves time, ensures reliability, accuracy, and security in the developed applications. Mashup application developers integrate these available APIs into their applications. Still, they have to go through thousands of available web APIs and chose only a few appropriate ones for their application. Recommending relevant web APIs might help application developers in this situation. However, very …

Model Development And Assessment Of The Gate Network In A High-Performance Sic Power Module, William Austin Curbow

Graduate Theses and Dissertations

The main objective of this effort is to determine points of weakness in the gate network of a high-performance SiC power module and to offer remedies to these issues to increase the overall performance, robustness, and reliability of the technology. In order to accomplish this goal, a highly accurate model of the gate network is developed through three methods of parameter extraction: calculation, simulation, and measurement. A SPICE model of the gate network is developed to analyze four electrical issues in a high-speed, SiC-based power module including the necessary internal gate resistance for damping under-voltage and over-voltage transients, the disparity …

Automatic Performance Optimization On Heterogeneous Computer Systems Using Manycore Coprocessors, Chenggang Lai

Graduate Theses and Dissertations

Emerging computer architectures and advanced computing technologies, such as Intel’s Many Integrated Core (MIC) Architecture and graphics processing units (GPU), provide a promising solution to employ parallelism for achieving high performance, scalability and low power consumption. As a result, accelerators have become a crucial part in developing supercomputers. Accelerators usually equip with different types of cores and memory. It will compel application developers to reach challenging performance goals. The added complexity has led to the development of task-based runtime systems, which allow complex computations to be expressed as task graphs, and rely on scheduling algorithms to perform load balancing between …

Dynamic 3d Network Data Visualization, Brok Stafford

Computer Science and Computer Engineering Undergraduate Honors Theses

Monitoring network traffic has always been an arduous and tedious task because of the complexity and sheer volume of network data that is being consistently generated. In addition, network growth and new technologies are rapidly increasing these levels of complexity and volume. An effective technique in understanding and managing a large dataset, such as network traffic, is data visualization. There are several tools that attempt to turn network traffic into visual stimuli. Many of these do so in 2D space and those that are 3D lack the ability to display network patterns effectively. Existing 3D network visualization tools lack user …

Improving The Efficacy Of Context-Aware Applications, Jon C. Hammer

Graduate Theses and Dissertations

In this dissertation, we explore methods for enhancing the context-awareness capabilities of modern computers, including mobile devices, tablets, wearables, and traditional computers. Advancements include proposed methods for fusing information from multiple logical sensors, localizing nearby objects using depth sensors, and building models to better understand the content of 2D images.

First, we propose a system called Unagi, designed to incorporate multiple logical sensors into a single framework that allows context-aware application developers to easily test new ideas and create novel experiences. Unagi is responsible for collecting data, extracting features, and building personalized models for each individual user. We demonstrate the …

Bayesian Network Modeling And Inference Of Gwas Catalog, Qiuping Pan

Graduate Theses and Dissertations

Genome-wide association studies (GWASs) have received an increasing attention to understand genotype-phenotype relationships. The Bayesian network has been proposed as a powerful tool for modeling single-nucleotide polymorphism (SNP)-trait associations due to its advantage in addressing the high computational complex and high dimensional problems. Most current works learn the interactions among genotypes and phenotypes from the raw genotype data. However, due to the privacy issue, genotype information is sensitive and should be handled by complying with specific restrictions. In this work, we aim to build Bayesian networks from publicly released GWAS statistics to explicitly reveal the conditional dependency between SNPs and …

Operating System Identification By Ipv6 Communication Using Machine Learning Ensembles, Adrian Ordorica

Graduate Theses and Dissertations

Operating system (OS) identification tools, sometimes called fingerprinting tools, are essential for the reconnaissance phase of penetration testing. While OS identification is traditionally performed by passive or active tools that use fingerprint databases, very little work has focused on using machine learning techniques. Moreover, significantly more work has focused on IPv4 than IPv6. We introduce a collaborative neural network ensemble that uses a unique voting system and a random forest ensemble to deliver accurate predictions. This approach uses IPv6 features as well as packet metadata features for OS identification. Our experiment shows that our approach is valid and we achieve …

Evaluating The Intrinsic Similarity Between Neural Networks, Stephen Charles Ashmore

Graduate Theses and Dissertations

We present Forward Bipartite Alignment (FBA), a method that aligns the topological structures of two neural networks. Neural networks are considered to be a black box, because neural networks contain complex model surface determined by their weights that combine attributes non-linearly. Two networks that make similar predictions on training data may still generalize differently. FBA enables a diversity of applications, including visualization and canonicalization of neural networks, ensembles, and cross-over between unrelated neural networks in evolutionary optimization. We describe the FBA algorithm, and describe implementations for three applications: genetic algorithms, visualization, and ensembles. We demonstrate FBA's usefulness by comparing a …

Wi-Fi Sensing Algorithms Utilizing Zigbee Rf Receiver For Use In Emergency Communications Mesh, Alexander H. Nelson

Inquiry: The University of Arkansas Undergraduate Research Journal

This thesis examines a low-power Wi-Fi sensing wake-up controller for an emergency communications mesh network; the goal of the research study is to progressively develop a prototype system that could be used in a live environment. Wireless network protocols are reviewed, in addition to a limited view of cluster analysis, in order to introduce relevant receiver concepts crucial to understanding this study. Algorithms for system implementation are developed, and pseudocode, designed to be configurable and platform-independent, is given for each. The system’s design goals are identified, followed by a discussion on approaches and optimizations in order to maximize the system’s …