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Exploratory Study on the Impact of Blockchain Adoption on Inventory Accuracy and Supply Chain Efficiency
No full textThis exploratory study delves into the factors shaping organizations\u27 adoption of Blockchain Technology (BCT) in supply chain management under inventory accuracy and supply chain efficiency. Through expert interviews, insights into the challenges, opportunities, and implications of integrating blockchain technology into supply chain operations are uncovered. The study aims to contribute actionable recommendations for organizations seeking to leverage blockchain solutions effectively. Ultimately, it seeks to advance theoretical understanding and inform practical decision-making in the evolving landscape of blockchain technology and supply chain management
Exploring the Impact of Blockchain Integration on Inventory Accuracy and Supply Chain Efficiency – A Literature Review
No full textBlockchain technology has emerged as a transformative solution offering strategic advantages across diverse sectors. This paper aims to give an overview of existing literature on how blockchain is integrated into supply chain management and its impact on inventory accuracy and efficiency. Our contributions include identifying key areas where blockchain affects inventory accuracy and uncovering the theories that drive its strategic integration. Additionally, we propose a new schematic framework that combines strategic organizational theories with the key areas that contribute towards inventory accuracy and supply chain efficiency. We recommend specific future directions related to blockchain adoption and its impact on inventory accuracy
The Trajectory of Romance Scams in the U.S.
Get PDFRomance scams (RS) inflict financial and emotional damage by defrauding victims under the guise of meaningful relationships. This research study examines RS trends in the U.S. through a quantitative analysis of web searches, news articles, research publications, and government reports from 2004–2023. This is the first study to use multiple sources for RS trend analysis. Results reveal increasing public interest and media coverage contrasted by a recent decrease in incidents reported to authorities. The frequency of research dedicated to RS has steadily grown but focuses predominantly on documenting the problem rather than developing solutions. Overall, findings suggest RS escalation despite declining official reports, which are likely obscured by low victim reporting rates. This highlights the need for greater awareness to encourage reporting enabling accurate data-driven policy responses. Additionally, more research must focus on techniques to counter these crimes. With improved awareness and prevention, along with responses informed by more accurate data, the rising RS threat can perhaps be mitigated
A Survey of Unikernel Security: Insights and Trends from a Quantitative Analysis
Get PDFUnikernels, an evolution of LibOSs, are emerging as a virtualization technology to rival those currently used by cloud providers. Unikernels combine the user and kernel space into one ``uni\u27\u27fied memory space and omit functionality that is not necessary for its application to run, thus drastically reducing the required resources. The removed functionality is significant however, and includes components that have become common security technologies such as Address Space Layout Randomization (ASLR), Data Execution Prevention (DEP), and Non-executable bits (NX bits). This raises questions about the security of unikernels. This research presents a quantitative methodology using TF-IDF to analyze the focus of security discussions within unikernel research literature. An initial corpus of 51 unikernel-related papers spanning 2013-2023 was collected. The systematic selection process detailed in the methodology narrowed down to 33 core papers which were then analyzed for trends in security topics. Analysis found that Memory Protection Extensions (MPX) and DEP were the least frequently occurring topics, while Software Guard Extensions (SGX) was the most frequent topic. The findings quantify priorities and assumptions in unikernel security research, identifying potential risks from underexplored attack surfaces. This study represents the first application of TF-IDF analysis to quantitatively assess trends in unikernel security literature, offering novel insights into the field\u27s development and focus areas. In addition, this approach should be broadly applicable for revealing trends and gaps in other niche security domains
Exploring an AI Chatbot to Close School Counselor Information Gaps: A Design Science Approach
Get PDFChatbots are an emerging technology that stimulate conversations with humans often using generative artificial intelligence (AI). This technology is becoming increasingly relevant, particularly in the field in school counseling. For school counselors facing growing demands, utilizing data to demonstrate the effectiveness of their services on student outcomes is crucial to secure recent position increases. Historically, school counselors have struggled with the expectation to use data and utilize available technology. AI chatbots have the potential to help school counselors, a traditionally non-technical audience, efficiently evaluate data, create goals, and explore potential interventions to accomplish those goals.
To support school counselors in the data-driven decision-making process and explore the various factors and design principles that influence satisfaction and continuance intention in a non-technical field, this research employs a Design Science Research Methodology to design and evaluate an AI chatbot grounded in principles of the Information Gap Theory and the Post-Acceptance Model of Information Systems Continuance. The proposed artifact is demonstrated by implementing a single case study to evaluate a school counselor’s satisfaction and continuance intention after using the chatbot. The findings of the quantitative analysis reveal curiosity has a significant influence on perceived usefulness of the AI chatbot and perceived usefulness is the greatest predictor of continuance intention. Curiosity was stimulated in users by revealing gaps in their current knowledge, increasing awareness of the system’s potential benefits, and making the interaction pleasurable. Results of the qualitative analysis reveal for an AI chatbot to be adopted by school counselors, it must optimize their workload, establish clear expectations, encourage ongoing interaction by asking curiosity-inducing questions, use social comparison to gain support, and sustain user engagement by meeting user’s needs using open-ended closure. This research not only contributes to the field of Information Systems by exploring the influence of curiosity on user satisfaction and continuance intentions using a DSRM, but also assists designers and developers by identifying design principles and considerations of an AI chatbot. Additionally, the study contributes to school counseling by providing a technology that supports school counselors in data-driven decision-making, improves efficiency, and enhances services provided to students, all of which can reinforce the school counselor’s role in the education system
FAIDS: artificial intelligence developmental systems framework for predicting and preventing cyberattacks in supply chain networks
Get PDFCyber threats and attacks disrupt and damages supply chain networks (SCNs), which are complex and interlinked. Current methods to predict and prevent cyberattacks are inadequate and ineffective. This research proposes an AI developmental systems framework (FAIDS) to protect SCNs from cyberattacks. The framework has four components: (1) an AI threat intelligence system; (2) an AI risk assessment system; (3) an AI decision support system; and (4) an AI learning and adaptation system. The framework is tested on a simulated retail SCN. The results show that the framework can predict and prevent cyberattacks and improve the network\u27s resilience and security. The research provides a novel and comprehensive AI framework for cyber security (CS) and supply chain management. The research also discusses the framework\u27s limitations and challenges and suggests future research
Do online test proctoring services abide by standard data protections?
Get PDFIn the aftermath of the COVID-19 pandemic, schools adopted new software to allow for online learning. Online exam proctoring has seen rapid growth in both K-12 and higher education. The security of these suites is critical due to their extensive access. Online proctoring suites have the capabilities to assess and configure student devices, access the microphone and cam-era, and view student information in the scope of the exam. This case study investigates the security of data sent over the network using dynamic software analysis and network monitoring while using the Respondus Lockdown Browser.https://scholar.dsu.edu/research-symposium/1036/thumbnail.jp
ABCD: A Risk Management Framework for SCADA Systems
Get PDFSupervisory Control and Data Acquisition (SCADA) systems are used to run, monitor, and manage large-scale industrial operations. SCADA systems are frequently the target of attackers for political or financial gain due to their increasing exposure to catastrophic destruction. Historically, the overwhelming majority of SCADA networks were completely self-contained, depending on proprietary protocols and software. This has ceased to be the case. As more industrial control systems become networked, their intrinsic security becomes increasingly susceptible to attack. Despite the importance of SCADA systems and their wide adoption, their security flaws have yet to be addressed. According to SecurityScoreCard, more than three-quarters of manufacturing organizations have unpatched high-severity vulnerabilities in their systems, and nearly forty percent of these organizations, which include metals, machinery, appliances, electrical equipment, and transportation, were infected with malware in 2022 (SecurityScoreCard, 2022). Trellix\u27s 2023 Threat Report also reported that malware attacking manufacturers accounted for 12 percent of ransomware campaigns disclosed publicly in 2022 (Trellix, 2023). SynSaber, a security firm that specializes in industrial asset and network monitoring, conducted an analysis of 926 CVEs that were included in ICS advisories from the US Cybersecurity and Infrastructure Security Agency (CISA) during the second half of 2022 and found that 35% of them had no patch or remediation available from the vendor (SynSaber, 2022). Even though compromising these vital systems could lead to catastrophic injury and operating difficulties, their security is still an open subject. In this study, we proposed a risk management framework for safeguarding SCADA systems that is based on the concept of offensive security as a means of bolstering SCADA system overall security. The research proposes a four-step methodology for managing cyber risk in SCADA systems, including assessing, blocking, capturing, and defending, which corresponds to the four primary tasks of risk management: identifying, preventing, detecting, and responding to risk. The term ABCD framework is derived from the initial letter of each of the four stages proposed by the research as well as the model used to illustrate the framework. The primary emphasis areas of the framework are multi-step attack prediction and security awareness, both of which are accomplished by predicting attack behaviors using recommended algorithms. The model provides an intuitive and adaptable adversarial environment that enables the administrator to predict the security scenario in advance, thereby aiding in the preparation of incident response actions necessary to maintain network connectivity
Automated Dynamic Policy Generation for Access Control in the Internet of Things
Get PDFInternet of Things (IoT) is commonly utilized in domestic and industrial environments to automate various tasks. Due to this, an enormous amount of data is being generated and transmitted through IoT networks. These data may contain sensitive information depending on the context. Access control is one of the frontline security measures that any information system should adopt. The dynamic nature of the IoT requires access control policies should be able to adapt to their environments. However, it is very challenging to specify access control policies manually because of their dynamic nature. Current literature suggests the need for automating the process of policy generation. Machine Learning and Deep Learning techniques can enable the required automation. The main objective of this dissertation is to answer the following research questions: 1) How can we self-generate contextual access control policies for the Internet of Things during unforeseen situations? 2) What are the existing challenges while specifying dynamic policies for access control in IoT? 3) How realistic are the generated access control policies to be used in real-time situations? In this research, we proposed a mixed-method approach where we implemented and evaluated two baseline Tabular Generative Adversarial Network models. We evaluated the performance of the solution using two datasets, namely the CAV Policies and Amazon Access Logs datasets. We obtained different perspectives based on our experiments. The common findings that our results demonstrate are that the models were able to generate synthetic access control policies by training from the datasets, and the models were able to learn the background knowledge specified during training to generate policies without any constraint violation
Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI
Get PDFIn the rapidly evolving field of cybersecurity, ensuring the reproducibility of AI-driven research is critical to maintaining the reliability and integrity of security systems. This paper addresses the reproducibility crisis within the domain of adversarial robustness—a key area in AI-based cybersecurity that focuses on defending deep neural networks against malicious perturbations. Through a detailed case study, we attempt to validate results from prior work on certified robustness using the VeriGauge toolkit, revealing significant challenges due to software and hardware incompatibilities, version conflicts, and obsolescence. Our findings underscore the urgent need for standardized methodologies, containerization, and comprehensive documentation to ensure the reproducibility of AI models deployed in critical cybersecurity applications. By tackling these reproducibility challenges, we aim to contribute to the broader discourse on securing AI systems against advanced persistent threats, enhancing network and IoT security, and protecting critical infrastructure. This work advocates for a concerted effort within the research community to prioritize reproducibility, thereby strengthening the foundation upon which future cybersecurity advancements are built