1393 research outputs found
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Assessing Security Flaws in Modern Precision Farming Systems
Get PDFThe increasing interconnectivity of the digital age brings new vulnerabilities for cyber criminals and nation state threat actors alike. Every day, threat actors make millions of at-tacks against a variety of systems. Though not all these attacks prove successful, they all share the same goal of manipulating or extracting data from their targets. The dawn of digitalization in the agricultural industry finds itself under the same threats. Because agriculture sits within the category of critical infrastructure, digitizing this industry should be accompanied with special concern for implementing good security practices. At a basic level, good cyber security practice includes ensuring that data remains confidential to users and processes, ensuring that data remains unmodified by unauthorized methods, and ensuring the accessibility of the data. However, no system can be made completely secure. Some form of exploitation will always exist by which proprietary data of the customer, or the manufacturer, can be leaked or abused. Thus, this paper does not ask if vulnerabilities exist on on-board precision farming equipment, but rather it asks what level of risk these exploits possess. In other words, if a vulnerability becomes exploited, what access or data does the attacker gain and does its value equal the amount of effort required to obtain it? The likely answer exposes vulnerabilities that damage the finances or reputations of individual producers and manufactures, which may provide an equal level of danger to the industry if these attacks can be scaled up against multiple targets.https://scholar.dsu.edu/research-symposium/1032/thumbnail.jp
The Impact of Family Business Ownership and Involvement on Entrepreneurial Self-Identification
Get PDFIn this study I analyze the impact that family business ownership has on the tendency of individuals to identify as being entrepreneurial. Drawing data from the 1979 cohort of the National Longitudinal Survey of Youth, I use two-sample t-tests and logistic regression models to explore the relationship between personal entrepreneurial aspirations and having come from an environment involving a family business. As part of this analysis, I control for several demographic factors, such as cognition, gender, and ethnicity. The outcomes from this research highlight the importance of family business as a key factor in fostering entrepreneurial mindsets, suggesting that the experiences and cultural context provided by family-owned enterprises are instrumental in encouraging future generations of entrepreneurship.https://scholar.dsu.edu/research-symposium/1033/thumbnail.jp
Factors influencing blockchain implementation in supply chain management: An exploratory pilot study
Get PDFThis study presents findings from a pilot study investigating key factors influencing the successful implementation of blockchain technology in supply chain organizations. Based on data collected from five participants, the pilot study offers valuable insights into the complex dynamics surrounding blockchain implementation. Through semi-structured interviews and grounded theory analysis, the study identifies key factors such as user adoption and acceptance, data integrity and protection, organizational strategy, strategic leadership, training, change management, and a supportive ecosystem. The findings highlight the multifaceted nature of blockchain implementation challenges and underscore the importance of comprehensive strategies for fostering user acceptance, aligning organizational goals, and navigating regulatory and technical complexities. By serving as a preliminary exploration, this pilot study lays the groundwork for future research aimed at further exploring the intricacies of blockchain implementation in the supply chain domain
Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunities
No full textAs quantum computing continues to advance, the development of quantum-secure neural networks is crucial to prevent adversarial attacks. This paper proposes three quantum-secure design principles: (1) using post-quantum cryptography, (2) employing quantum-resistant neural network architectures, and (3) ensuring transparent and accountable development and deployment. These principles are supported by various quantum strategies, including quantum data anonymization, quantum-resistant neural networks, and quantum encryption. The paper also identifies open issues in quantum security, privacy, and trust, and recommends exploring adaptive adversarial attacks and auto adversarial attacks as future directions. The proposed design principles and recommendations provide guidance for developing quantum-secure neural networks, ensuring the integrity and reliability of machine learning models in the quantum era
Using LLMs to Establish Implicit User Sentiment of Software Desirability
Get PDFThis study explores the use of LLMs for providing quantitative zero-shot sentiment analysis of implicit software desirability, addressing a critical challenge in product evaluation where traditional review scores, though convenient, fail to capture the richness of qualitative user feedback. Innovations include establishing a method that 1) works with qualitative user experience data without the need for explicit review scores, 2) focuses on implicit user satisfaction, and 3) provides scaled numerical sentiment analysis, offering a more nuanced understanding of user sentiment, instead of simply classifying sentiment as positive, neutral, or negative.
Data is collected using the Microsoft Product Desirability Toolkit (PDT), a well-known qualitative user experience analysis tool. For initial exploration, the PDT metric was given to users of two software systems. PDT data was fed through several LLMs (Claude Sonnet 3 and 3.5, GPT4, and GPT4o) and through a leading transfer learning technique, Twitter-Roberta-Base-Sentiment, and Vader, a leading sentiment analysis tool. Each system was asked to evaluate the data in two ways, by looking at the sentiment expressed in the PDT word/explanation pairs; and by looking at the sentiment expressed by the users in their grouped selection of five words and explanations, as a whole. Numerical analysis is used to provide insights into the magnitude of sentiment to drive high quality decisions regarding product desirability. Each LLM is asked to provide its confidence (low, medium, high) in its sentiment score, along with an explanation of its score.
All LLMs tested were able to statistically detect user sentiment from the users\u27 grouped data, whereas TRBS and Vader were not. The confidence and explanation of confidence provided by the LLMs assisted in understanding user sentiment. This study adds deeper understanding of evaluating user experiences, toward the goal of creating a universal tool that quantifies implicit sentiment
Microsegmented Cloud Network Architecture Using Open-Source Tools for a Zero Trust Foundation
Get PDFThis paper presents a multi-cloud networking architecture built on zero trust principles and micro-segmentation to provide secure connectivity with authentication, authorization, and encryption in transit. The proposed design includes the multi-cloud network to support a wide range of applications and workload use cases, compute resources including containers, virtual machines, and cloud-native services, including IaaS (Infrastructure as a Service), PaaS (Platform as a service). Furthermore, open-source tools provide flexibility, agility, and independence from locking to one vendor technology. The paper provides a secure architecture with micro-segmentation and follows zero trust principles to solve multi-fold security and operational challenges
Terror from the Skies?: Investigating the energetics and feeding ecology of one of the largest pterosaurs: Quetzalcoatlus northropi
Get PDFOur goal is to assess the gut capacity and energetics requirements for Q. northropi in order to understand the role that it played when living.https://scholar.dsu.edu/research-symposium/1049/thumbnail.jp
Artificial Intelligence Usage and Data Privacy Discoveries within mHealth
Get PDFAdvancements in artificial intelligence continue to impact nearly every aspect of human life by providing integration options that aim to supplement or improve current processes. One industry that continues to benefit from artificial intelligence integration is healthcare. For years now, elements of artificial intelligence have been used to assist in clinical decision making, helping to identify potential health risks at earlier stages, and supplementing precision medicine. An area of healthcare that specifically looks at wearable devices, sensors, phone applications, and other such devices is mobile health (mHealth). These devices are used to aid in health data collection and delivery. This paper aims at addressing the current uses and challenges of artificial intelligence within the mHealth field as well as an overview of current methods to help provide patient data privacy during data collection and storage
Assessing Security Vulnerabilities in Wireless IoT Devices
Get PDFThis research project aims to systematically assess and analyze security vulnerabilities in accessibility technology, specifically devices related to the American Disabilities Act (ADA), such as wheelchair lifts, ADA buttons, and light switches. The project focuses on the potential risks associated with radio frequency (RF) replay attacks, a well-documented threat in IoT security. By examining the vulnerabilities and consequences of RF replay attacks in these critical areas, the research seeks to enhance the security and safety of individuals with disabilities and the broader public. The project also explores potential countermeasures and ethical considerations for responsible vulnerability disclosure, contributing to the fields of accessibility technology, IoT security, and ethical cybersecurity practices.https://scholar.dsu.edu/research-symposium/1029/thumbnail.jp
Identifying Critical Factors That Impact Learning Analytics Adoption by Higher Education Faculty
Get PDFHigher education institutions (HEI) are beginning to invest heavily in learning analytics as a compliment to their existing suite of technologies used to enhance the pedagogical practices of instructors. However, learning analytics continues to see low adoption and integration by higher education faculty. While a culture of learning analytics within HEI is emerging, there is not consensus on the value and effectiveness of the tools and practices that make up the culture. With promises of reduced student dropout rates, improved student outcomes, better course pedagogy and backed by pressures of assessment and accountability, learning analytics is being trumpeted as the next best solution to our educational woes. However, despite these promises, and despite the general belief that learning analytics may have true value, instructors have been slow, if not resistant, in learning analytics adoption. More research is needed to understand factors that either threaten or enable a higher education faculty member’s willingness to adopt learning analytics.
The following paper demonstrates how the technology-pedagogy-content knowledge framework (TPACK) can be used to extend traditional technology adoption models to include professional identity expectancy in an effort to explain intention to use behavior. A quantitative analysis using SEM techniques on 222 United States based survey respondents is used to inform results. The results support effort expectancy, performance expectancy, and professional identity expectancy to be key factors of willingness to adopt learning analytics. These results may inform additional research into the influence of professional identity expectancy on technology adoption as well as research, development, and marketing opportunities within the consumer space of learning analytics tools