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BYOD security issues: a systematic literature review
Get PDFOrganizations are exposed to new security risks when they allow employees’ personal mobile devices to access the network and the corporate data (a phenomenon called ‘Bring Your Own Device’ or BYOD). They are confronted with inherent security issues that need to be addressed in order to protect the organization and its information. What are the security issues and considerations associated with BYOD environments? With this in mind, the objective of this paper is to present a systematic literature review of scholarly literature (2010–2019) with respect to BYOD security, and to suggest a classification scheme that depicts a holistic approach to securing BYOD environments. The results of this review include the analysis of 38 scholarly articles, where 22 security issues were identified. Based on the proposed classification scheme, the analysis of the findings shows that 86% of the articles identified security issues and considerations associated with the IT domain, 51% identified security issues related to the Management domain, 45% related to the Users domain, and 19% related to the Mobile Device domain. The results also show that BYOD security issues corresponding to policies are among the most frequently addressed concerns, followed by network security, data protection, user’s attitude/behavior and governance
Fairness Challenges in Artificial Intelligence
No full textFairness is a highly desirable human value in day-to-day decisions that affect human life. In recent years many successful applications of AI systems have been developed, and increasingly, AI methods are becoming part of many new applications for decision-making tasks that were previously carried out by human beings. Questions have been raised 1) can the decision be trusted? 2) is it fair? Overall, are the AI-based systems making fair decisions, or are they increasing the unfairness in society? This chapter presents a systematic literature review (SLR) of existing works on AI fairness challenges. Towards this end, a conceptual bias mitigation framework for organizing and discussing AI fairness-related research is developed and presented. The systematic review provides a mapping of the AI fairness challenges to components of a proposed framework based on the suggested solutions within the literature. Future research opportunities are also identified
Compliance Based Penetration Testing as a Service
Get PDFThe current penetration testing method practiced in the information systems domain is insufficient to protect information systems. Penetration testing is part of the final acceptance criteria before the system is released into a production environment. Once the system is in production, the environment and configuration are bound to change for various reasons, especially in cloud environments. This change can create vulnerabilities, and hackers take advantage of them. In cloud service models like PaaS, security is a shared responsibility of tenant and provider, and it is challenging to perform penetration testing. This paper introduces a new method called Compliance Based Penetration Testing (CBPT). The CBPT method explicitly targets PaaS environments to identify critical issues in cloud-based environments. As the cloud is the way moving forward, this approach will be beneficial and save effort and cost for all cloud consumers
Open-Source Software Development: Mass Spectrometry Data Management Program Prototype
Get PDFEfficiency, data integrity, and communication of results are imperative to any discipline of scientific research. Hindering these fundamental aspects results in crippled lab productivity, inability to verify results, delayed publishing of research, and frustration among lab members. Such is the case in the Dakota State University chemistry lab, where research is focused on molecular identification via mass spectrometry. Proprietary software provided alongside the mass spectrometer instrument is incapable of properly reporting desired analysis outputs. The lack of neat, thorough data reports necessitates tedious, frustrating manual data handling by lab scientists that hinders research and increases chances for data corruption. To mitigate this, I am developing an in-lab auxiliary program that aims to make up for the shortcomings of the current proprietary software. This thesis looks into the development of such a program, with heavy emphasis on software engineering practices
Privacy and Security Concerns Associated with mHealth Technologies: A Computational Social Science Approach
Get PDFFirst Report of Aphanomyces euteiches Race 1 and Race 2 Causing Aphanomyces Root Rot on Alfalfa (Medicago sativa) in South Dakota
No full textAcademic Hall of Fame
Get PDFDr. Eric Johnson\u27s legacy at Dakota State University has touched every academic department. Dr. Johnson was part of the cohort of faculty tasked with designing new majors and programs after the new mission of technology and computer education was adopted in 1984. As part of this task force, he along with a few other faculty attended an IBM coding seminar during the summer of 1983. The primary purpose of this trip was to learn directly from IBM developers and managers what skills students would need to find jobs in the computing world. He authored the program proposal, A Brand New Day, detailing eleven new and modified academic programs for Dakota State College, many of which are the foundation of DSU\u27s current catalog. In addition to his contributions to DSU\u27s mission, Dr. Johnson was at the cutting edge of his field of English, developing software that could automate literary text analysis. He was director of the Conference on Symbolic and Logical Computing, focused on software development to aid in humanities research, and he was lead editor for TEXT Technology, a journal for research in the field of computer text analysis
IoT Device Identification Using Supervised Machine Learning
No full textInternet of Things (IoT) has been increasingly becoming mainstream and can be considered as the next stage of the internet revolution. The increasing use of IoT-based applications presents several issues to massively connected devices. For example, companies and organizations need to have a fast and reliable way to identify IoT devices on their networks to manage access and prevent vulnerable devices from connecting. On the other hand, machine learning has been widely used for image processing, intrusion detection, and malware classification. However, there are few studies on device identification using machine learning. In this paper, we propose a machine learning-assisted approach for IoT device identification. That includes four essential components: network traffic collection, feature extraction, data labeling, and machine learning. We test and evaluate four machine learning classifiers in a testing network, including multiple IoT devices. The evaluation results indicate a 79% accuracy in identifying the IoT devices in the considered network testbed
The Role of the Privacy Calculus and the Privacy Paradox in the Acceptance of Wearables for Health and Wellbeing
No full textThe Internet along with innovations in technology have inspired an industry focused on designing portable devices, known as wearables that can track users’ personal activities and wellbeing. While such technologies have many benefits, they also have risks (especially regarding information privacy and security). These concerns become even more pronounced with healthcare-related wearables. Consequently, users must consider the benefits given the risks (privacy calculus); however, users often opt for wearables despite their disclosure concerns (privacy paradox). In this study, we investigate the multidimensional role that privacy (and, in particular, the privacy calculus and the privacy paradox) plays in consumers’ intention to disclose their personal information, whether health status has a moderating effect on the relationship, and the influence of privacy on acceptance. To do so, we evaluated a research model that explicitly focused on the privacy calculus and the privacy paradox in the healthcare wearables acceptance domain. We used a survey-oriented approach to collect data from 225 users and examined relationships among privacy, health, and acceptance constructs. In that regard, our research confirmed significant evidence of the influence of the privacy calculus on disclosure and acceptance as well as evidence of the privacy paradox when considering health status. We found that consumers felt less inclined to disclose their personal information when the risks to privacy outweighed benefits; however, health status moderated this behavior such that people with worse health tipped the scale towards disclosure. This study expands our previous knowledge about healthcare wearables’ privacy/acceptance paradigm and, thus, the influences that affect healthcare wearables’ acceptance in the privacy context
