Computer Engineering Commons^™

Joint Linear And Nonlinear Computation With Data Encryption For Efficient Privacy-Preserving Deep Learning, Qiao Zhang

Electrical & Computer Engineering Theses & Dissertations

Deep Learning (DL) has shown unrivalled performance in many applications such as image classification, speech recognition, anomalous detection, and business analytics. While end users and enterprises own enormous data, DL talents and computing power are mostly gathered in technology giants having cloud servers. Thus, data owners, i.e., the clients, are motivated to outsource their data, along with computationally-intensive tasks, to the server in order to leverage the server’s abundant computation resources and DL talents for developing cost-effective DL solutions. However, trust is required between the server and the client to finish the computation tasks (e.g., conducting inference for the newly-input …

Data-Driven Operational And Safety Analysis Of Emerging Shared Electric Scooter Systems, Qingyu Ma

Computational Modeling & Simulation Engineering Theses & Dissertations

The rapid rise of shared electric scooter (E-Scooter) systems offers many urban areas a new micro-mobility solution. The portable and flexible characteristics have made E-Scooters a competitive mode for short-distance trips. Compared to other modes such as bikes, E-Scooters allow riders to freely ride on different facilities such as streets, sidewalks, and bike lanes. However, sharing lanes with vehicles and other users tends to cause safety issues for riding E-Scooters. Conventional methods are often not applicable for analyzing such safety issues because well-archived historical crash records are not commonly available for emerging E-Scooters.

Perceiving the growth of such a micro-mobility …

Protection Of Patient Privacy On Mobile Device Machine Learning, Matthew Nguyen

Cybersecurity Undergraduate Research Showcase

An existing StudentLife Study mobile dataset was evaluated and organized to be applied to different machine learning methods. Different variables like user activity, exercise, sleep, study space, social, and stress levels are optimized to train a model that could predict user stress level. The different machine learning methods would test if both patient data privacy and training efficiency can be ensured.

Effects Of Cloud Computing In The Workforce, Kevin Rossi Acosta

Cybersecurity Undergraduate Research Showcase

In recent years, the incorporation of cloud computing and cloud services has increased in many different types of organizations and companies. This paper will focus on the philosophical, economical, and political factors that cloud computing and cloud services have in the workforce and different organizations. Based on various scholarly articles and resources it was observed that organizations used cloud computing and cloud services to increase their overall productivity as well as decrease the overall cost of their operations, as well as the different policies that were created by lawmakers to control the realm of cloud computing. The results of this …

Feature Extraction And Design In Deep Learning Models, Daniel Perez

Computational Modeling & Simulation Engineering Theses & Dissertations

The selection and computation of meaningful features is critical for developing good deep learning methods. This dissertation demonstrates how focusing on this process can significantly improve the results of learning-based approaches. Specifically, this dissertation presents a series of different studies in which feature extraction and design was a significant factor for obtaining effective results. The first two studies are a content-based image retrieval system (CBIR) and a seagrass quantification study in which deep learning models were used to extract meaningful high-level features that significantly increased the performance of the approaches. Secondly, a method for change detection is proposed where the …

Wg2An: Synthetic Wound Image Generation Using Generative Adversarial Network, Salih Sarp, Murat Kuzlu, Emmanuel Wilson, Ozgur Guler

Engineering Technology Faculty Publications

In part due to its ability to mimic any data distribution, Generative Adversarial Network (GAN) algorithms have been successfully applied to many applications, such as data augmentation, text-to-image translation, image-to-image translation, and image inpainting. Learning from data without crafting loss functions for each application provides broader applicability of the GAN algorithm. Medical image synthesis is also another field that the GAN algorithm has great potential to assist clinician training. This paper proposes a synthetic wound image generation model based on GAN architecture to increase the quality of clinical training. The proposed model is trained on chronic wound datasets with various …

Role Of Artificial Intelligence In The Internet Of Things (Iot) Cybersecurity, Murat Kuzlu, Corinne Fair, Ozgur Guler

Engineering Technology Faculty Publications

In recent years, the use of the Internet of Things (IoT) has increased exponentially, and cybersecurity concerns have increased along with it. On the cutting edge of cybersecurity is Artificial Intelligence (AI), which is used for the development of complex algorithms to protect networks and systems, including IoT systems. However, cyber-attackers have figured out how to exploit AI and have even begun to use adversarial AI in order to carry out cybersecurity attacks. This review paper compiles information from several other surveys and research papers regarding IoT, AI, and attacks with and against AI and explores the relationship between these …

Hidden Markov Model And Cyber Deception For The Prevention Of Adversarial Lateral Movement, Md Ali Reza Al Amin, Sachin Shetty, Laurent Njilla, Deepak K. Tosh, Charles Kamhoua

Computational Modeling & Simulation Engineering Faculty Publications

Advanced persistent threats (APTs) have emerged as multi-stage attacks that have targeted nation-states and their associated entities, including private and corporate sectors. Cyber deception has emerged as a defense approach to secure our cyber infrastructure from APTs. Practical deployment of cyber deception relies on defenders' ability to place decoy nodes along the APT path optimally. This paper presents a cyber deception approach focused on predicting the most likely sequence of attack paths and deploying decoy nodes along the predicted path. Our proposed approach combines reactive (graph analysis) and proactive (cyber deception technology) defense to thwart the adversaries' lateral movement. The …

A Monte-Carlo Analysis Of Monetary Impact Of Mega Data Breaches, Mustafa Canan, Omer Ilker Poyraz, Anthony Akil

Engineering Management & Systems Engineering Faculty Publications

The monetary impact of mega data breaches has been a significant concern for enterprises. The study of data breach risk assessment is a necessity for organizations to have effective cybersecurity risk management. Due to the lack of available data, it is not easy to obtain a comprehensive understanding of the interactions among factors that affect the cost of mega data breaches. The Monte Carlo analysis results were used to explicate the interactions among independent variables and emerging patterns in the variation of the total data breach cost. The findings of this study are as follows: The total data breach cost …

Human Characteristics Impact On Strategic Decisions In A Human-In-The-Loop Simulation, Andrew J. Collins, Shieda Etemadidavan

Engineering Management & Systems Engineering Faculty Publications

In this paper, a hybrid simulation model of the agent-based model and cooperative game theory is used in a human-in-the-loop experiment to study the effect of human demographic characteristics in situations where they make strategic coalition decisions. Agent-based modeling (ABM) is a computational method that can reveal emergent phenomenon from interactions between agents in an environment. It has been suggested in organizational psychology that ABM could model human behavior more holistically than other modeling methods. Cooperative game theory is a method that models strategic coalitions formation. Three characteristics (age, education, and gender) were considered in the experiment to see if …

Human Factors, Ergonomics And Industry 4.0 In The Oil & Gas Industry: A Bibliometric Analysis, Francesco Longo, Antonio Padovano, Lucia Gazzaneo, Jessica Frangella, Rafael Diaz

VMASC Publications

Over the last few years, the Human Factors and Ergonomics (HF/E) discipline has significantly benefited from new human-centric engineered digital solutions of the 4.0 industrial age. Technologies are creating new socio-technical interactions between human and machine that minimize the risk of design-induced human errors and have largely contributed to remarkable improvements in terms of process safety, productivity, quality, and workers’ well-being. However, despite the Oil&Gas (O&G) sector is one of the most hazardous environments where human error can have severe consequences, Industry 4.0 aspects are still scarcely integrated with HF/E. This paper calls for a holistic understanding of the changing …

Developing An Artificial Intelligence Framework To Assess Shipbuilding And Repair Sub-Tier Supply Chains Risk, Rafael Diaz, Katherine Smith, Beatriz Acero, Francesco Longo, Antonio Padovano

VMASC Publications

The defense shipbuilding and repair industry is a labor-intensive sector that can be characterized by low-product volumes and high investments in which a large number of shared resources, technology, suppliers, and processes asynchronously converge into large construction projects. It is mainly organized by the execution of a complex combination of sequential and overlapping stages. While entities engaged in this large-scale endeavor are often knowledgeable about their first-tier suppliers, they usually do not have insight into the lower tiers suppliers. A sizable part of any supply chain disruption is attributable to instabilities in sub-tier suppliers. This research note conceptually delineates a …

Simulation For Cybersecurity: State Of The Art And Future Directions, Hamdi Kavak, Jose J. Padilla, Daniele Vernon-Bido, Saikou Y. Diallo, Ross Gore, Sachin Shetty

VMASC Publications

In this article, we provide an introduction to simulation for cybersecurity and focus on three themes: (1) an overview of the cybersecurity domain; (2) a summary of notable simulation research efforts for cybersecurity; and (3) a proposed way forward on how simulations could broaden cybersecurity efforts. The overview of cybersecurity provides readers with a foundational perspective of cybersecurity in the light of targets, threats, and preventive measures. The simulation research section details the current role that simulation plays in cybersecurity, which mainly falls on representative environment building; test, evaluate, and explore; training and exercises; risk analysis and assessment; and humans …

On The Usage And Vulnerabilities Of Api Systems, Conner D. Yu

Cybersecurity Undergraduate Research Showcase

To some, Application Programming Interface (API) is one of many buzzwords that seem to be blanketed in obscurity because not many people are overly familiar with this term. This obscurity is unfortunate, as APIs play a crucial role in today’s modern infrastructure by serving as one of the most fundamental communication methods for web services. Many businesses use APIs in some capacity, but one often overlooked aspect is cybersecurity. This aspect is most evident in the 2018 misuse case by Facebook, which led to the leakage of 50 million users’ records.1 During the 2018 Facebook data breach incident, threat actors …

Blockchain For A Resilient, Efficient, And Effective Supply Chain, Evidence From Cases, Adrian Gheorghe, Farinaz Sabz Ali Pour, Unal Tatar, Omer Faruk Keskin

Engineering Management & Systems Engineering Faculty Publications

In the modern acquisition, it is unrealistic to consider single entities as producing and delivering a product independently. Acquisitions usually take place through supply networks. Resiliency, efficiency, and effectiveness of supply networks directly contribute to the acquisition system's resiliency, efficiency, and effectiveness. All the involved firms form a part of a supply network essential to producing the product or service. The decision-makers have to look for new methodologies for supply chain management. Blockchain technology introduces new methods of decentralization and delegation of services, which can transform supply chains and result in a more resilient, efficient, and effective supply chain. This …

Ship Deck Segmentation In Engineering Document Using Generative Adversarial Networks, Mohammad Shahab Uddin, Raphael Pamie-George, Daron Wilkins, Andres Sousa Poza, Mustafa Canan, Samuel Kovacic, Jiang Li

Engineering Management & Systems Engineering Faculty Publications

Generative adversarial networks (GANs) have become very popular in recent years. GANs have proved to be successful in different computer vision tasks including image-translation, image super-resolution etc. In this paper, we have used GAN models for ship deck segmentation. We have used 2D scanned raster images of ship decks provided by US Navy Military Sealift Command (MSC) to extract necessary information including ship walls, objects etc. Our segmentation results will be helpful to get vector and 3D image of a ship that can be later used for maintenance of the ship. We applied the trained models to engineering documents provided …

Converting Optical Videos To Infrared Videos Using Attention Gan And Its Impact On Target Detection And Classification Performance, Mohammad Shahab Uddin, Reshad Hoque, Kazi Aminul Islam, Chiman Kwan, David Gribben, Jiang Li

Electrical & Computer Engineering Faculty Publications

To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can have a lot more optical videos than infrared videos. This lack of IR video datasets can be mitigated if optical-to-infrared video conversion is possible. In this paper, we present a new approach for converting optical videos to infrared videos using deep learning. The basic idea is to focus on target areas using attention generative adversarial network (attention GAN), which will preserve the fidelity of target areas. …

Covid-19 And Biocybersecurity's Increasing Role On Defending Forward, Xavier Palmer, Lucas N. Potter, Saltuk Karahan

Electrical & Computer Engineering Faculty Publications

The evolving nature of warfare has been changing with cybersecurity and the use of advanced biotechnology in each aspect of the society is expanding and overlapping with the cyberworld. This intersection, which has been described as “biocybersecurity” (BCS), can become a major front of the 21st-century conflicts. There are three lines of BCS which make it a critical component of overall cybersecurity: (1) cyber operations within the area of BCS have life threatening consequences to a greater extent than other cyber operations, (2) the breach in health-related personal data is a significant tool for fatal attacks, and (3) health-related misinformation …