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2021

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Articles 3031 - 3060 of 3475

Full-Text Articles in Computer Sciences

Predictive Maintenance Of Bearing Machinery Using Simulation- A Bibliometric Study, Karan Gulati Mr., Keshav Basandrai Mr., Shubham Tiwari Mr., Pooja Kamat Prof., Satish Kumar Dr. Jan 2021

Predictive Maintenance Of Bearing Machinery Using Simulation- A Bibliometric Study, Karan Gulati Mr., Keshav Basandrai Mr., Shubham Tiwari Mr., Pooja Kamat Prof., Satish Kumar Dr.

Library Philosophy and Practice (e-journal)

Modelling is a way of constructing a virtual representation of software and hardware that involves a real-world device. We will discover the behaviour of the system if the software elements of this model are guided by mathematical relationships. For testing conditions that may be difficult to replicate with hardware prototypes alone, modelling and simulation are particularly useful, especially in the early phase of the design process when hardware might not be available. Model-based approach in MATLAB-Simulink can be useful for predictive maintenance of machines as it can reduce unplanned downtimes and maintenance costs when industrial equipment breaks. Through this bibliometric …

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Just-In-Time Pastureland Trait Estimation For Silage Optimization, Under Limited Data Constraints, Patricia O'Byrne Jan 2021

Just-In-Time Pastureland Trait Estimation For Silage Optimization, Under Limited Data Constraints, Patricia O'Byrne

Doctoral

To ensure that pasture-based farming meets production and environmental targets for a growing population under increasing resource constraints, producers need to know pastureland traits. Current proximal pastureland trait prediction methods largely rely on vegetation indices to determine biomass and moisture content. The development of new techniques relies on the challenging task of collecting labelled pastureland data, leading to small datasets. Classical computer vision has already been applied to weed identification and recognition of fruit blemishes using morphological features, but machine learning algorithms can parameterise models without the provision of explicit features, and deep learning can extract even more abstract knowledge …

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Proxy-Free Privacy-Preserving Task Matching With Efficient Revocation In Crowdsourcing, Jiangang Shu, Kan Yang, Xiaohua Jia, Ximeng Liu, Cong Wang, Robert H. Deng Jan 2021

Proxy-Free Privacy-Preserving Task Matching With Efficient Revocation In Crowdsourcing, Jiangang Shu, Kan Yang, Xiaohua Jia, Ximeng Liu, Cong Wang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Task matching in crowdsourcing has been extensively explored with the increasing popularity of crowdsourcing. However, privacy of tasks and workers is usually ignored in most of exiting solutions. In this paper, we study the problem of privacy-preserving task matching for crowdsourcing with multiple requesters and multiple workers. Instead of utilizing proxy re-encryption, we propose a proxy-free task matching scheme for multi-requester/multi-worker crowdsourcing, which achieves task-worker matching over encrypted data with scalability and non-interaction. We further design two different mechanisms for worker revocation including ServerLocal Revocation (SLR) and Global Revocation (GR), which realize efficient worker revocation with minimal overhead on the …

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Adversarial Specification Mining, Hong Jin Kang, David Lo Jan 2021

Adversarial Specification Mining, Hong Jin Kang, David Lo

Research Collection School Of Computing and Information Systems

There have been numerous studies on mining temporal specifications from execution traces. These approaches learn finite-state automata (FSA) from execution traces when running tests. To learn accurate specifications of a software system, many tests are required. Existing approaches generalize from a limited number of traces or use simple test generation strategies. Unfortunately, these strategies may not exercise uncommon usage patterns of a software system. To address this problem, we propose a new approach, adversarial specification mining, and develop a prototype, DICE (Diversity through Counter-Examples). DICE has two components: DICE-Tester and DICE-Miner. After mining Linear Temporal Logic specifications from an input …

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Novel Techniques In Recovering, Embedding, And Enforcing Policies For Control-Flow Integrity, Yan Lin Jan 2021

Novel Techniques In Recovering, Embedding, And Enforcing Policies For Control-Flow Integrity, Yan Lin

Dissertations and Theses Collection (Open Access)

Control-Flow Integrity (CFI) is an attractive security property with which most injected and code-reuse attacks can be defeated, including advanced attacking techniques like Return-Oriented Programming (ROP). CFI extracts a control-flow graph (CFG) for a given program and instruments the program to respect the CFG. Specifically, checks are inserted before indirect branch instructions. Before these instructions are executed during runtime, the checks consult the CFG to ensure that the indirect branch is allowed to reach the intended target. Hence, any sort of controlflow hijacking would be prevented. There are three fundamental components in CFI enforcement. The first component is accurately recovering …

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Scaling Up Exact Neural Network Compression By Relu Stability, Thiago Serra, Xin Yu, Abhinav Kumar, Srikumar Ramalingam Jan 2021

Scaling Up Exact Neural Network Compression By Relu Stability, Thiago Serra, Xin Yu, Abhinav Kumar, Srikumar Ramalingam

Faculty Conference Papers and Presentations

We can compress a rectifier network while exactly preserving its underlying functionality with respect to a given input domain if some of its neurons are stable. However, current approaches to determine the stability of neurons with Rectified Linear Unit (ReLU) activations require solving or finding a good approximation to multiple discrete optimization problems. In this work, we introduce an algorithm based on solving a single optimization problem to identify all stable neurons. Our approach is on median 183 times faster than the state-of-art method on CIFAR-10, which allows us to explore exact compression on deeper (5 x 100) and wider …

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The Enlightening Role Of Explainable Artificial Intelligence In Chronic Wound Classification, Salih Sarp, Murat Kuzlu, Emmanuel Wilson, Umit Cali, Ozgur Guler Jan 2021

The Enlightening Role Of Explainable Artificial Intelligence In Chronic Wound Classification, Salih Sarp, Murat Kuzlu, Emmanuel Wilson, Umit Cali, Ozgur Guler

Engineering Technology Faculty Publications

Artificial Intelligence (AI) has been among the most emerging research and industrial application fields, especially in the healthcare domain, but operated as a black-box model with a limited understanding of its inner working over the past decades. AI algorithms are, in large part, built on weights calculated as a result of large matrix multiplications. It is typically hard to interpret and debug the computationally intensive processes. Explainable Artificial Intelligence (XAI) aims to solve black-box and hard-to-debug approaches through the use of various techniques and tools. In this study, XAI techniques are applied to chronic wound classification. The proposed model classifies …

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Biocybersecurity: A Converging Threat As An Auxiliary To War, Lucas Potter, Orlando Ayala, Xavier-Lewis Palmer Jan 2021

Biocybersecurity: A Converging Threat As An Auxiliary To War, Lucas Potter, Orlando Ayala, Xavier-Lewis Palmer

Engineering Technology Faculty Publications

Biodefense is the discipline of ensuring biosecurity with respect to select groups of organisms and limiting their spread. This field has increasingly been challenged by novel threats from nature that have been weaponized such as SARS, Anthrax, and similar pathogens, but has emerged victorious through collaboration of national and world health groups. However, it may come under additional stress in the 21st century as the field intersects with the cyberworld-- a world where governments have already been struggling to keep up with cyber attacks from small to state-level actors as cyberthreats have been relied on to level the playing field …

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Food/Non-Food Classification Of Real-Life Egocentric Images In Low- And Middle-Income Countries Based On Image Tagging Features, Guangzong Chen, Wenyan Jia, Yifan Zhao, Zhi-Hong Mao, Benny Lo, Alex K Anderson, Gary Frost, Modou L Jobarteh, Megan A Mccrory, Edward Sazonov, Matilda Steiner-Asiedu, Richard S Ansong, Thomas Baranowski, Lora Burke, Mingui Sun Jan 2021

Food/Non-Food Classification Of Real-Life Egocentric Images In Low- And Middle-Income Countries Based On Image Tagging Features, Guangzong Chen, Wenyan Jia, Yifan Zhao, Zhi-Hong Mao, Benny Lo, Alex K Anderson, Gary Frost, Modou L Jobarteh, Megan A Mccrory, Edward Sazonov, Matilda Steiner-Asiedu, Richard S Ansong, Thomas Baranowski, Lora Burke, Mingui Sun

Children’s Nutrition Research Center Staff Publications

Malnutrition, including both undernutrition and obesity, is a significant problem in low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa. Our assessment includes multiple diet-related activities in urban and rural families, including food sources (e.g., shopping, harvesting, and gathering), preservation/storage, preparation, cooking, and consumption (e.g., portion size and nutrition analysis). …

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An Ensemble Approach For Annotating Source Code Identifiers With Part-Of-Speech Tags, Christian D. Newman,, Michael J. Decker, Reem S. Alsuhaibani, Anthony Peruma, Mohamed Wiem Mkaouer, Satyajit Mohapatra, Tejal Vishnoi, Marcos Zampieri, Timothy Sheldon, Emily Hill Jan 2021

An Ensemble Approach For Annotating Source Code Identifiers With Part-Of-Speech Tags, Christian D. Newman,, Michael J. Decker, Reem S. Alsuhaibani, Anthony Peruma, Mohamed Wiem Mkaouer, Satyajit Mohapatra, Tejal Vishnoi, Marcos Zampieri, Timothy Sheldon, Emily Hill

Articles

This paper presents an ensemble part-of-speech tagging approach for source code identifiers. Ensemble tagging is a technique that uses machine-learning and the output from multiple part-of-speech taggers to annotate natural language text at a higher quality than the part-of-speech taggers are able to obtain independently. Our ensemble uses three state-of-the-art part-of-speech taggers: SWUM, POSSE, and Stanford. We study the quality of the ensemble's annotations on five different types of identifier names: function, class, attribute, parameter, and declaration statement at the level of both individual words and full identifier names. We also study and discuss the weaknesses of our tagger to …

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Lstm-Based Model For Human Brain Decisions Using Eeg Signals Analysis, Lorela Bano Jan 2021

Lstm-Based Model For Human Brain Decisions Using Eeg Signals Analysis, Lorela Bano

College of Graduate Studies: Theses & Dissertations

As machine learning models become more sophisticated, and biometric data becomes more readily available through new non-invasive technologies, it becomes increasingly possible to gain access to interesting biometric data that could revolutionize Human Computer Interaction. In this research, we propose a framework to assess and quantify human preference (like or dislike) on presenting various external visual stimuli. Our framework relies on an Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) based model and on electroencephalogram (EEG) signals analysis to predict Like or Dislike preference of human subjects when presented with various marketing images.

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Leadership Strategies For Implementing Telemedicine Technology In Rural Hospitals To Improve Profitability, Chikezie Ralph Waturuocha Jan 2021

Leadership Strategies For Implementing Telemedicine Technology In Rural Hospitals To Improve Profitability, Chikezie Ralph Waturuocha

Walden Dissertations and Doctoral Studies

Ineffective leadership strategies can negatively impact telemedicine technology implementation in rural hospitals, which may hinder profitability. Rural hospital administrators who struggle to implement telemedicine technology for improved profitability are at high risk of experiencing patient health outcomes failure. Grounded in the technology acceptance model, the purpose of this qualitative single case study was to explore leadership strategies rural hospital leaders use to implement telemedicine technology to improve profitability. The participants were five health care administrators of a rural hospital in a Midwest U.S. state who successfully implemented telemedicine technology to improve profitability. Sources for data collection were semistructured interviews and …

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Information Technology Resources For Precision Medicine, Nicholas L. Bertram Jan 2021

Information Technology Resources For Precision Medicine, Nicholas L. Bertram

Walden Dissertations and Doctoral Studies

Healthcare delivery organizations have an opportunity to use insights from the emerging field of precision medicine to improve the quality of patient care; however, information technology resources to fully enable precision medicine are lacking. The specific problem was that people have limited information to use when making decisions regarding information technology resources for precision medicine in healthcare delivery organizations given the emerging state of precision medicine. The purpose of this Delphi study was to determine how a panel of precision medicine information technology experts view information technology resource importance and feasibility for precision medicine in healthcare delivery organizations. The research …

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Exploring The Value Of Technology Within Cross-Departmental Communications, Brian J. Luckey Jan 2021

Exploring The Value Of Technology Within Cross-Departmental Communications, Brian J. Luckey

Walden Dissertations and Doctoral Studies

While many midsized businesses have invested in technology to support business operations, most have not realized the potential value of using technology to collaborate cross-departmentally. There is a lack of knowledge concerning strategies for using technology to facilitate effective organizational communications, which has resulted in operating technology investments being made without corresponding investments in communication technologies. The purpose of this qualitative case study was to fill the knowledge gap concerning the impact of technology for cross-departmental communications. The theoretical foundation for this study was based on systems theory, organizational theory, and stakeholder theory. The key research question involved the impact …

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Insider Threats' Behaviors And Data Security Management Strategies, Gladys C. Cooley Jan 2021

Insider Threats' Behaviors And Data Security Management Strategies, Gladys C. Cooley

Walden Dissertations and Doctoral Studies

As insider threats and data security management concerns become more prevalent, the identification of risky behaviors in the workplace is crucial for the privacy of individuals and the survival of organizations. The purpose of this three-round qualitative Delphi study was to identify real-time consensus among 25 information technology (IT) subject matter experts (SMEs) in the Washington metropolitan area about insider threats and data security management. The SMEs participating in this study were adult IT professionals and senior managers with certification in their area of specialization and at least 5 years of practical experience. The dark triad theory was the conceptual …

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The Digitization Of Court Processes In African Regional And Subregional Judicial Institutions, Frederic Drabo Jan 2021

The Digitization Of Court Processes In African Regional And Subregional Judicial Institutions, Frederic Drabo

Walden Dissertations and Doctoral Studies

Despite information technology (IT) officers’ multiple efforts to develop reliable and efficient electronic justice (e-justice) systems, digitizing court processes still presents several quality challenges associated with IT infrastructure and literacy issues. Grounded in the principles of total quality management, the purpose of this qualitative multiple case study was to identify strategies and best practices IT officers in African regional economic communities (REC) use for digitizing regional and subregional court processes to improve African e-justice systems. The participants included four IT officers working as assistant computer system analysts (ACSA), computer system analysts (CSA), and heads of IT (HIT) in regional and …

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A Multicase Study Of Critical Success Factors Of Self-Service Business Intelligence Initiatives, Eva Shepherd Jan 2021

A Multicase Study Of Critical Success Factors Of Self-Service Business Intelligence Initiatives, Eva Shepherd

Walden Dissertations and Doctoral Studies

Information technology (IT) managers have sparse information on the critical success factors (CSFs) needed for self-service business intelligence (SSBI) initiatives among casual users. The purpose of this qualitative, multicase study was to describe Business Intelligence (BI) experts’ views on the CSFs needed for self-service BI initiatives among casual users in the post-implementation stage. To meet this purpose, a multicase study design was used to collect data from a purposeful sample of 10 BI experts. Semistructured interviews, archival data, and reflective field notes drove the credibility of the multicase study’s findings through data triangulation. Two conceptual models framed this study: Lennerholt …

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Design And Deployment Of A Mobile Learning Cloud Network To Facilitate Open Educational Resources For Asynchronous Learning, Joselito Christian Paulus M. Villanueva, Mark Anthony V. Melendres, Catherine Genevieve B. Lagunzad, Nathaniel Joseph C. Libatique Jan 2021

Design And Deployment Of A Mobile Learning Cloud Network To Facilitate Open Educational Resources For Asynchronous Learning, Joselito Christian Paulus M. Villanueva, Mark Anthony V. Melendres, Catherine Genevieve B. Lagunzad, Nathaniel Joseph C. Libatique

Biology Faculty Publications

This paper describes the design and deployment of a mobile cloud network that facilitates open educational resource content distribution. The setup utilized clustered single board computers as content, communication and monitoring servers. It was installed in a Public High School where stakeholders, using their mobile devices, were given access to preloaded content via wireless local area network. Initial tests of the mobile cloud showed good network performance. Teachers were randomly selected to evaluate the content validity and delivery of the OER content. Results show that the quality of the network’s OER content is very satisfactory. This implementation shows the advantage …

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Learning Accurate And Robust Deep Visual Models, Yandong Li Jan 2021

Learning Accurate And Robust Deep Visual Models, Yandong Li

Electronic Theses and Dissertations, 2020-2023

Over the last decade, we have witnessed the renaissance of deep neural networks (DNNs) and their successful applications in computer vision. There is still a long way to build intelligent and reliable machine vision systems, but DNNs provide a promising direction. The goal of this thesis is to present a few small steps along this road. We mainly focus on two questions: How to design label-efficient learning algorithms for computer vision tasks? How to improve the robustness of DNN based visual models? Concerning label-efficiency, we investigate a reinforced sequential model for video summarization, a background hallucination strategy for high-resolution image …

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Implication Of Manifold Assumption In Deep Learning Models For Computer Vision Applications, Marzieh Edraki Jan 2021

Implication Of Manifold Assumption In Deep Learning Models For Computer Vision Applications, Marzieh Edraki

Electronic Theses and Dissertations, 2020-2023

The Deep Neural Networks (DNN) have become the main contributor in the field of machine learning (ML). Specifically in the computer vision (CV), there are applications like image and video classification, object detection and tracking, instance segmentation and visual question answering, image and video generation are some of the applications from many that DNNs have demonstrated magnificent progress. To achieve the best performance, the DNNs usually require a large number of labeled samples, and finding the optimal solution for such complex models with millions of parameters is a challenging task. It is known that, the data are not uniformly distributed …

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Visual Learning Beyond Human Curated Datasets, Muhammad Abdullah Jamal Jan 2021

Visual Learning Beyond Human Curated Datasets, Muhammad Abdullah Jamal

Electronic Theses and Dissertations, 2020-2023

The success of deep neural networks in a variety of computer vision tasks heavily relies on large- scale datasets. However, it is expensive to manually acquire labels for large datasets. Given the human annotation cost and scarcity of data, the challenge is to learn efficiently with insufficiently labeled data. In this dissertation, we propose several approaches towards data-efficient learning in the context of few-shot learning, long-tailed visual recognition, and unsupervised and semi-supervised learning. In the first part, we propose a novel paradigm of Task-Agnostic Meta- Learning (TAML) algorithms to improve few-shot learning. Furthermore, in the second part, we analyze the …

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Spatial-Temporal Representation Learning: Concepts, Algorithms And Applications, Pengyang Wang Jan 2021

Spatial-Temporal Representation Learning: Concepts, Algorithms And Applications, Pengyang Wang

Electronic Theses and Dissertations, 2020-2023

Recent years have witnessed the flourish of Internet-of-Things (IoT), in which sensors connect spatial entities to constitute complex Cyber-Physical Systems (CPSs). In this setting, spatial-temporal data becomes increasingly available. Mining spatial-temporal data can reveal holistic user and system structures, dynamics, and semantics of the underlying CPSs, including identifying trends, forecasting future behavior, and detecting anomalies. However, obtaining effective representations over spatial-temporal data remains a big challenge for the following reasons: (1) on the one hand, traditional manual feature design is labor-intensive and time-consuming facing the complex and huge volumes of spatial-temporal data; (2) on the other hand, as an emerging …

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Distributed Cross-Community Collaboration For The Cloud-Based Energy Management Service, Yu-Wen Chen, J. Morris Chang Jan 2021

Distributed Cross-Community Collaboration For The Cloud-Based Energy Management Service, Yu-Wen Chen, J. Morris Chang

Publications and Research

Customers’ participation is a critical factor for inte-grating the distributed energy resources via demand response and demand-side management programs, especially when customers become prosumers. Incentives need to be delivered by the energy management service to attract prosumers to operate their distributed energy resources and electricity loads grid-friendly actively. The cloud-based energy management service enables virtual trading for customers within the same community to minimize cost and smooth the fluctuation. With the potential fast-growing number of service providers and customers, the needs exist for efficiently collaborating across multiple service providers and customers. This paper proposes the distributed cross-community collaboration (XCC) for …

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Information Systems: No Boundaries! A Concise Approach To Understanding Information Systems For All Disciplines, Shane M. Schartz Jan 2021

Information Systems: No Boundaries! A Concise Approach To Understanding Information Systems For All Disciplines, Shane M. Schartz

All Open Educational Resources

This book was created to provide a different experience for students beginning their studies in information systems. Instead of being bombarded with information from a business systems perspective, the goal of this book is to provide a baseline of material regarding information systems in all disciplines, not just business systems - hence the name No Boundaries!

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Lightweight Encryption Based Security Package For Wireless Body Area Network, Sangwon Shin Jan 2021

Lightweight Encryption Based Security Package For Wireless Body Area Network, Sangwon Shin

Electronic Theses and Dissertations

As the demand of individual health monitoring rose, Wireless Body Area Networks (WBAN) are becoming highly distinctive within health applications. Nowadays, WBAN is much easier to access then what it used to be. However, due to WBAN’s limitation, properly sophisticated security protocols do not exist. As WBAN devices deal with sensitive data and could be used as a threat to the owner of the data or their family, securing individual devices is highly important. Despite the importance in securing data, existing WBAN security methods are focused on providing light weight security methods. This led to most security methods for WBAN …

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Ensemble Encoder-Decoder Models For Predicting Land Transformation, Pariya Pourmohammadi Jan 2021

Ensemble Encoder-Decoder Models For Predicting Land Transformation, Pariya Pourmohammadi

Graduate Theses, Dissertations, and Problem Reports (ETD)

In studying dynamic and complex processes which are influenced by a system of inter-connected driving variables, it is crucial to apply models that can learn the complexity of the interactions. Land transformation is one of such complex processes, prediction of which can help to mitigate severe climate situations and improve the resiliency of communities. In this study, a multi-spectral set of data cubes is used to capture various characteristics of a geographic region. Based on the data cube, a feature space is constructed using socio-economic attributes, terrain characteristics, and landscape traits of the study region. Two-dimensional and three-dimensional convolutional neural …

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Deep Fingerprint Matching From Contactless To Contact Fingerprints For Increased Interoperability, Alexander James Wilson Jan 2021

Deep Fingerprint Matching From Contactless To Contact Fingerprints For Increased Interoperability, Alexander James Wilson

Graduate Theses, Dissertations, and Problem Reports (ETD)

Contactless fingerprint matching is a common form of biometric security today. Most smartphones and associated apps now let users opt into using this form of biometric security. However, it’s difficult to match a finger-photo to a fingerprint because of perspective distortion occurring at the edges of the finger-photo, so direct matching using conventional methods will not be as accurate due to a lack of sufficient matching minutiae points. To address this issue, we propose a deep model, Perspective Distortion Rectification Model (PDRM), to estimate the fingerprint correspondence for finger-photo images in order to recover more minutiae points. Not only do …

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Maidrl: Semi-Centralized Multi-Agent Reinforcement Learning Using Agent Influence, Anthony Lee Harris Jan 2021

Maidrl: Semi-Centralized Multi-Agent Reinforcement Learning Using Agent Influence, Anthony Lee Harris

Graduate Theses/Dissertations

In recent years, reinforcement learning algorithms, a subset of machine learning that focuses on solving problems through trial-and-error learning, have been used in the field of multi-agent systems to help the agents with interactions and cooperation on a variety of tasks. Given the enormous success of reinforcement learning in single-agent systems like Chess, Shogi, and Go, it is natural for the next step to be the expansion into multi-agent systems. However, controlling multiple agents simultaneously is extremely challenging, as the complexity increases tremendously with the number of agents in the system. Existing approaches in this regard use a wide range …

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Association Of Incident Cancer To Low-Value Care And Healthcare Cost Burden Among Elderly Medicare Beneficiaries, Chibuzo Iloabuchi Jan 2021

Association Of Incident Cancer To Low-Value Care And Healthcare Cost Burden Among Elderly Medicare Beneficiaries, Chibuzo Iloabuchi

Graduate Theses, Dissertations, and Problem Reports (ETD)

In the United States (US), 25% of healthcare spending is considered wasteful because it is spent reimbursing low-value care. Low-value care is the utilization of healthcare services, medical tests, and procedures that have unclear or no clinical benefit to patients but still exposes them to risk. World-wide, low-value care imposes a significant economic burden on patients, payers, governments, and society. Cancer care among older adults > 65 years is one of the biggest drivers of healthcare expenditure in the US and accounts for nearly 40% of all spending, and low-value care among cancer patients is prevalent and contributes to the financial …

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Extending Critical Infrastructure Element Longevity Using Constellation-Based Id Verification, Christopher M. Rondeau, Michael A. Temple, J. Addison Betances, Christine M. Schubert Kabban Jan 2021

Extending Critical Infrastructure Element Longevity Using Constellation-Based Id Verification, Christopher M. Rondeau, Michael A. Temple, J. Addison Betances, Christine M. Schubert Kabban

Faculty Publications

This work supports a technical cradle-to-grave protection strategy aimed at extending the useful lifespan of Critical Infrastructure (CI) elements. This is done by improving mid-life operational protection measures through integration of reliable physical (PHY) layer security mechanisms. The goal is to improve existing protection that is heavily reliant on higher-layer mechanisms that are commonly targeted by cyberattack. Relative to prior device ID discrimination works, results herein reinforce the exploitability of constellation-based PHY layer features and the ability for those features to be practically implemented to enhance CI security. Prior work is extended by formalizing a device ID verification process that …

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