Digital Commons Network^™

An Analysis Of Successful Sqlia For Future Evolutionary Prediction, Andrew Pechin

Senior Honors Theses

Web applications are a fundamental component of the internet, many interact with backend databases. Securing web applications and their databases from hackers should be a top priority for cybersecurity researchers. Structured Query Language (SQL) injection attacks (SQLIA) constitute a significant threat to web applications. They can hijack the backend databases to steal personally identifiable information (PII), initiate scams, or launch more sophisticated cyberattacks. SQLIA has evolved since its conception in the early 2000s and will continue to do so in the coming years. This paper analyzes past literature and successful SQLIA from specific time periods to identify themes and methods …

Regulating Artificial Intelligence In International Investment Law, Mark Mclaughlin

Research Collection Yong Pung How School Of Law

The interaction between artificial intelligence (AI) and international investment treaties is an uncharted territory of international law. Concerns over the national security, safety, and privacy implications of AI are spurring regulators into action around the world. States have imposed restrictions on data transfer, utilised automated decision-making, mandated algorithmic transparency, and limited market access. This article explores the interaction between AI regulation and standards of investment protection. It is argued that the current framework provides an unpredictable legal environment in which to adjudicate the contested norms and ethics of AI. Treaties should be recalibrated to reinforce their anti-protectionist origins, embed human-centric …

Threat Construction And Framing Of Cyberterrorism In The U.S. News Media, Mehmet F. Bastug, Ismail Onat, Ahmet Guler

International Journal of Cybersecurity Intelligence & Cybercrime

This research aims to explore the influence of news media on the fear of cyberterrorism and how cyberterrorism is framed in the media. Using a mixed-method approach as a research strategy, this paper reports on two studies that explore the influence of news reading on the fear of cyberterrorism. The first study analyzed survey responses from 1,190 participants and found that increased exposure to reading news media was associated with increased fear of cyberterrorism. The second study, built on the first, sought to investigate how cyberterrorism is framed and constructed as a threat by the US local and national newspapers. …

Adaptation Of Network Flow Problems For Course Of Action Generation, Alexander N. Stephens

Theses and Dissertations

This thesis introduces two methods to generate Courses of Action (COA) in distributed warfare scenarios: the Wargaming Commodity Course of Action Automated Method Under Uncertainty (WCCAAM-U2) and Dynamic Transshipment Problem (DTP)-generated COAs. Previous work by Deberry et al. used a Multi-Commodity Flow Problem (MCFP) to generate COAs for single-period wargame scenarios with known enemy force amounts. In WCCAAM-U2, we adapt an MCFP to work in situations where only intelligence estimates of enemy forces are known. Compared to two other COA-generation methods, the WCCAAAM-U2 COA outperforms the next highest-performing COA by 307% when compared by a ratio of objective success rate …

Classification And Analysis Of Twitter Bot And Troll Accounts, Callan P. Mccormick

Theses and Dissertations

This research trains, tests, and analyzes bot and troll classification models using publicly available, open source datasets. Specifically, it applies decision tree, random forest, feed forward neural networks, and long-short term memory neural networks with hyperparameters tuned via designed experiment to five labeled bot datasets created between 2011 and 2020 and one dataset labeling state-sponsored disinformation accounts or trolls. The first three models utilize account profile features, while the last model applies natural language processing techniques, specifically GloVe embedding, to analyze a user’s Tweet history. Results indicate that the random forest model outperforms the other three models with an average …

Air Force Digital Badges, Jacob Chan

Theses and Dissertations

The Air Force talent management and force development systems are antiquated. Airmen records are often stored on different Air Force information systems. Existing records sometimes lack granularity and context to recognize Airmen skills. Digital badges are a newer technology utilized by academia and industry to recognize member skills. However, military badging research is sparse and existing studies do not provide sufficient evidence on the value of digital badging to the Air Force. The studies: (1) lack background research on badging; (2) do not provide quantitative data on the effects of badging; and (3) issued badges through commercial entities which may …

Developing And Assessing A Generalized Serious Game That Supports Customized Joint All-Domain Operations Related Learning Objectives, Jonathan D. Moore

Theses and Dissertations

As the threat of near-peer adversaries has increased, the DoD has increased its emphasis on Joint All-Domain Operations (JADO). This emphasis on JADO highlights the need for hands-on training that can engage military members at all levels. The serious game Battlespace Next (BSN) was designed to teach high-level JADO concepts by modeling real-world military assets in the context of a strategic card game. To keep pace with the evolving landscape of warfare as well as fit the needs of a variety of Department of Defense (DoD) communities, this research introduces the Battlespace Next Education Framework (BSNEF). The BSNEF allows JADO …

Cellphone-Acoustics Based Suas Detection And Tracking, Ryan D. Clendening

Theses and Dissertations

Small Unmanned Aerial Systems (sUAS) are an easily accessible technology that has become an increasingly large threat to US critical systems. This threatening technology demands using fault-tolerant, low-cost, replaceable, and accurate sensing resources, which counter the ubiquitous nature of sUAS [1]. Therefore, the methods developed in this thesis detect and track sUAS using easily accessible sensing resources, such as cellphones. First, we develop an acoustics sensor network-based sUAS detection methodology. In the latter effort, a deep learning model is trained using the acoustics data from the data collection to predict sUAS range from a cellphone. Combined, these two efforts demonstrate …

Strategic Action Execution Through Regret Matching In Press Diplomacy, Leif D. White

Theses and Dissertations

To take most advantage of collaboration, negotiation is paramount to succeed in press Diplomacy. Humans use this construct to work towards self victory or sometimes towards an alternative strategic objective undefined in the game’s rules. To emulate this behavior, this thesis examines how to use communication to enable the victory or defeat of any other player in the game. This research develops a press Diplomacy agent, Lyre, that can work to attain these specific objectives in Diplomacy through the regret matching algorithm (RM). We also study how Lyre can begin Diplomacy with the goal to win, then shift strategies to …

Simulation And Analysis Of Dynamic Threat Avoidance Routing In An Anti-Access Area Denial (A2ad) Environment, Dante C. Reid

Theses and Dissertations

This research modeled and analyzed the effectiveness of different routing algorithms for penetration assets in an A2AD environment. AFSIM was used with different configurations of SAMs locations and numbers to compare the performance of AFSIM’s internal zone and shrink algorithm routers with a Dijkstra algorithm router. Route performance was analyzed through computational and operational metrics, including computational complexity, run-time, mission survivability, and simulation duration. This research also analyzed the impact of the penetration asset’s ingress altitude on those factors. Additionally, an excursion was conducted to analyze the Dijkstra algorithm router’s grid density holding altitude constant to understand its impact on …

Temporal Tensor Factorization For Multidimensional Forecasting, Jackson Cates, Karissa Scipke, Randy Hoover, Kyle Caudle

SDSU Data Science Symposium

In the era of big data, there is a need for forecasting high-dimensional time series that might be incomplete, sparse, and/or nonstationary. The current research aims to solve this problem for two-dimensional data through a combination of temporal matrix factorization (TMF) and low-rank tensor factorization. From this method, we propose an expansion of TMF to two-dimensional data: temporal tensor factorization (TTF). The current research aims to interpolate missing values via low-rank tensor factorization, which produces a latent space of the original multilinear time series. We then can perform forecasting in the latent space. We present experimental results of the proposed …

An Efficient Hash-Based Assessment And Recovery Algorithm For Distributed Healthcare Systems, Sanaa Kaddoura, Ramzi Haraty, Sultan Al Jahdali, Mohamad Jaber

All Works

The enhancement of the information technology in many domains has had a positive impact on the healthcare sector. The ability to share medical data is one of the positive outcomes. However, this improvement comes with a number of threats. Although many threat preventive measures have been applied yet, no one can be confident that the system is safe from attacks. Thus, an algorithm needs to assess the damage occurring as a result of an attack before recovering the database. In this work, we present a distributed algorithm that uses hash tables to deal with the “information warfare” problem in healthcare …

The Open Charge Point Protocol (Ocpp) Version 1.6 Cyber Range A Training And Testing Platform, David Elmo Ii

Browse all Theses and Dissertations

The widespread expansion of Electric Vehicles (EV) throughout the world creates a requirement for charging stations. While Cybersecurity research is rapidly expanding in the field of Electric Vehicle Infrastructure, efforts are impacted by the availability of testing platforms. This paper presents a solution called the “Open Charge Point Protocol (OCPP) Cyber Range.” Its purpose is to conduct Cybersecurity research against vulnerabilities in the OCPP v1.6 protocol. The OCPP Cyber Range can be used to enable current or future research and to train operators and system managers of Electric Charge Vehicle Supply Equipment (EVSE). This paper demonstrates this solution using three …

Towards Explainable Ai Using Attribution Methods And Image Segmentation, Garrett J. Rocks

Honors Undergraduate Theses

With artificial intelligence (AI) becoming ubiquitous in a broad range of application domains, the opacity of deep learning models remains an obstacle to adaptation within safety-critical systems. Explainable AI (XAI) aims to build trust in AI systems by revealing important inner mechanisms of what has been treated as a black box by human users. This thesis specifically aims to improve the transparency and trustworthiness of deep learning algorithms by combining attribution methods with image segmentation methods. This thesis has the potential to improve the trust and acceptance of AI systems, leading to more responsible and ethical AI applications. An exploratory …

Landmark Stereo Dataset For Landmark Recognition And Moving Node Localization In A Non-Gps Battlefield Environment, Ganesh Sapkota, Sanjay Madria

Computer Science Faculty Research & Creative Works

In this paper, we have proposed a new strategy of using the landmark anchor node instead of a radio-Based anchor node to obtain the virtual coordinates (landmarkID, DISTANCE) of moving troops or defense forces that will help in tracking and maneuvering the troops along a safe path within a GPS-denied battlefield environment. the proposed strategy implements landmark recognition using the Yolov5 model and landmark distance estimation using an efficient Stereo Matching Algorithm. We consider that a moving node carrying a low-power mobile device facilitated with a calibrated stereo vision camera that can capture stereo images of a scene containing landmarks …

Identification And Modeling Social Media Influence Pathways: A Characterization Of A Disinformation Campaign Using The Flooding-The-Zone Strategy Via Transfer Entropy, Jasser Jasser

Electronic Theses and Dissertations, 2020-2023

The internet has made it easy for narratives to spread quickly and widely without regard for accuracy or the harm they may cause to society. Unfortunately, this has led to the rise of bad actors who use fake and misleading articles to spread harmful misinformation. These actors flood the information space with low-quality articles in an effort to disrupt opposing narratives, sow confusion, and discourage the pursuit of truth. In societies that prioritize free speech, maintaining control over the information space remains a persistent challenge. Achieving this requires strategic planning to protect the dissemination of information in ways that promote …

Patch-Wise Training With Convolutional Neural Networks To Synthetically Upscale Cfd Simulations, John P. Romano, Alec C. Brodeur, Oktay Baysal

Mechanical & Aerospace Engineering Faculty Publications

This paper expands the authors’ prior work[1], which focuses on developing a convolutional neural network (CNN) model capable of mapping time-averaged, unsteady Reynold’s-averaged Navier-Stokes (URANS) simulations to higher resolution results informed by time-averaged detached eddy simulations (DES). The authors present improvements over the prior CNN autoencoder model that result from hyperparameter optimization, increased data set augmentation through the adoption of a patch-wise training approach, and the predictions of primitive variables rather than vorticity magnitude. The training of the CNN model developed in this study uses the same URANS and DES simulations of a transonic flow around several NACA 4-digit airfoils …

Anti-American Stance In Turkey: A Twitter Case Study, Gowri Prathap, Alex Korb, Luke Palmieri, Ekrem Kaya, Saltuk Karahan, Hamdi Kavak

School of Cybersecurity Faculty Publications

The availability of social media and biased actors exacerbated Anti-American and Anti-Western views to extremes. In this paper, we report our efforts in analyzing anti-American views on Twitter. We have collected over three years of Turkish tweets related to the US, translated them into English, and analyzed these tweets using various computational social science tools. We found that Turkish tweets related to the US are significantly negative, and emotions reflect disgust and anger. Furthermore, we found that the source of the negative views stems from political actors like Trump or Biden rather than general hatred. Our results shed light on …

Commentary On Healthcare And Disruptive Innovation, Hilary Finch, Affia Abasi-Amefon, Woosub Jung, Lucas Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

Exploits of technology have been an issue in healthcare for many years. Many hospital systems have a problem with “disruptive innovation” when introducing new technology. Disruptive innovation is “an innovation that creates a new market by applying a different set of values, which ultimately overtakes an existing market” (Sensmeier, 2012). Modern healthcare systems are historically slow to accept new technological advancements. This may be because patient-based, provider-based, or industry-wide decisions are tough to implement, giving way to dire consequences. One potential consequence is that healthcare providers may not be able to provide the best possible care to patients. For example, …

Explainable Ai For Android Malware Detection, Maithili Kulkarni

Master's Projects

Android malware detection based on machine learning (ML) is widely used by the mobile device security community. Machine learning models offer benefits in terms of detection accuracy and efficiency, but it is often difficult to understand how such models make decisions. As a result, popular malware detection strategies remain black box models, which may result in a lack of accountability and trust in the decisions made. The field of explainable artificial intelligence (XAI) attempts to shed light on such black box models. In this research, we apply XAI techniques to ML-based Android malware detection systems. We train classic ML models …

The Future Between Quantum Computing And Cybersecurity, Daniel Dorazio

Williams Honors College, Honors Research Projects

Quantum computing, a novel branch of technology based on quantum theory, processes information in ways beyond the capabilities of classical computers. Traditional computers use binary digits [bits], but quantum computers use quantum binary digits [qubits] that can exist in multiple states simultaneously. Since developing the first two-qubit quantum computer in 1998, the quantum computing field has experienced rapid growth.

Cryptographic algorithms such as RSA and ECC, essential for internet security, rely on the difficulty of complex math problems that classical computers can’t solve. However, the advancement of quantum technology threatens these encryption systems. Algorithms, such as Shor’s, leverage the power …

Convolutional-Neural-Network-Based Des-Level Aerodynamic Flow Field Generation From Urans Data, John P. Romano, Oktay Baysal, Alec C. Brodeur

Mechanical & Aerospace Engineering Faculty Publications

The present paper culminates several investigations into the use of convolutional neural networks (CNNs) as a post-processing step to improve the accuracy of unsteady Reynolds-averaged Navier–Stokes (URANS) simulations for subsonic flows over airfoils at low angles of attack. Time-averaged detached eddy simulation (DES)-generated flow fields serve as the target data for creating and training CNN models. CNN post-processing generates flow-field data comparable to DES resolution, but after using only URANS-level resources and properly training CNN models. This document outlines the underlying theory and progress toward the goal of improving URANS simulations by looking at flow predictions for a class of …

S-400s, Disinformation, And Anti-American Sentiment In Turkey, Russell "Alex" Korb, Saltuk Karahan, Gowri Prathap, Ekrem Kaya, Luke Palmieri, Hamdi Kavak, Richard L. Wilson (Ed.), Major Brendan Curran (Ed.)

Political Science & Geography Faculty Publications

As social and political discourse in most countries becomes more polarized, anti-Americanism has risen not only in the Middle East and Latin America but also among the U.S. allies in Europe. Social media is one platform used to disseminate anti-American views in NATO countries, and its effectiveness can be magnified when mass media, public officials, and popular figures adopt these views. Disinformation, in particular, has gained recognition as a cybersecurity issue from 2016 onward, but disinformation can be manufactured domestically in addition to being part of a foreign influence campaign. In this paper, we analyze Turkish tweets using sentiment analysis …

Humans In The Loop, Nicholson Price Ii, Rebecca Crootof, Margot Kaminski

Articles

From lethal drones to cancer diagnostics, humans are increasingly working with complex and artificially intelligent algorithms to make decisions which affect human lives, raising questions about how best to regulate these “human in the loop” systems. We make four contributions to the discourse.

First, contrary to the popular narrative, law is already profoundly and often problematically involved in governing human-in-the-loop systems: it regularly affects whether humans are retained in or removed from the loop. Second, we identify “the MABA-MABA trap,” which occurs when policymakers attempt to address concerns about algorithmic incapacities by inserting a human into decision making process. Regardless …

Deep-Learning-Based Classification Of Digitally Modulated Signals Using Capsule Networks And Cyclic Cumulants, John A. Snoap, Dimitrie C. Popescu, James A. Latshaw, Chad M. Spooner

Electrical & Computer Engineering Faculty Publications

This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper …

Social-Engineering, Bio-Economies, And Nation-State Ontological Security: A Commentary, Brandon Griffin, Keitavius Alexander, Xavier-Lewis Palmer, Lucas Potter

Electrical & Computer Engineering Faculty Publications

Biocybersecurity is an evolving discipline that aims to identify the gaps and risks associated with the convergence of Biology (the science of life and living organisms) and cybersecurity (the science, study, and theory of cyberspace and cybernetics) to protect the bioeconomy. The biological industries’ increased reliance on digitization, automation, and computing power has resulted in benefits for the scientific community, it has simultaneously multiplied the risk factors associated with industrial espionage and the protection of data both commercial and proprietary. The sensitive and potentially destructive power of this data and its access inherently poses a risk to the national and …

Implications Of Cyberbiosecurity In Advanced Agriculture, Simone Stephen, Keitavius Alexander, Lucas Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

The world is currently undergoing a rapid digital transformation sometimes referred to as the fourth industrial revolution. During this transformation, it is increasingly clear that many scientific fields are not prepared for this change. One specific area is agriculture. As the sector which creates global food supply, this critical infrastructure requires detailed assessment and research via newly developed technologies (Millett et al, 2019; Peccoud et al, 2018) . Despite its fundamental significance to modern civilization, many aspects of industrial agriculture have not yet adapted to the digital world. This is evident in the many vulnerabilities currently present within agricultural systems, …

A Reflection Of Typology And Verification Flaws In Consideration Of Biocybersecurity/Cyberbiosecurity: Just Another Gap In The Wall, Luke Potter, Kim Mossberg, Xavier Palmer

Electrical & Computer Engineering Faculty Publications

Verification is central to any process in a functional and enduring cyber-secure organization. This verification is how the validity or accuracy of a state of being is assessed (Schlick, 1936; Balci, 1998). Conversely, breakdown in verification procedures is core to the interruption of normal operations for an organization. A key problem for organizations that utilize biology as an interlock within their systems is that personnel lack sufficient ability to verify all practically relevant biological information for procedures such as a nurse logging a blood draw, or a molecular biology technician preparing agar to culture microbes for study. This has several …

Cyber-Aidd: A Novel Approach To Implementing Improved Cyber Security Resilience For Large Australian Healthcare Providers Using A Unified Modelling Language Ontology, Martin Dart, Mohiuddin Ahmed

Research outputs 2022 to 2026

Purpose: This paper proposes a novel cyber security risk governance framework and ontology for large Australian healthcare providers, using the structure and simplicity of the Unified Modelling Language (UML). This framework is intended to mitigate impacts from the risk areas of: (1) cyber-attacks, (2) incidents, (3) data breaches, and (4) data disclosures. Methods Using a mixed-methods approach comprised of empirical evidence discovery and phenomenological review, existing literature is sourced to confirm baseline ontological definitions. These are supplemented with Australian government reports, professional standards publications and legislation covering cyber security, data breach reporting and healthcare governance. Historical examples of healthcare cyber …

Design And Implementation Of A Graphql Mesh Gateway: Federating Api Endpoints Based On A Defined Data Model, Marcus D. Scese

Dissertations, Master's Theses and Master's Reports

This paper introduces the GraphQL Mesh federated API (Application Programming Interface) gateway project, a comprehensive initiative implemented using GraphQL Mesh to solve data related issues within the USW-DSS (Undersea Warfare - Decision Support System). The project contributes to the evolving discourse on the pivotal role of Data Fabrics and Data Meshes in dismantling the barriers imposed by digital data silos. The project is a collaboration between researchers at Michigan Technological University, and engineers at ARiA (Applied Research in Acoustics LLC). The aim of the project is to resolve difficulties in understanding a large collection of API endpoints. By navigating the …