Browsing by Author "Adjerid, Idris"
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- Improving vulnerability remediation through better exploit predictionJacobs, Jay; Romanosky, Sasha; Adjerid, Idris; Baker, Wade (2020-09-14)Despite significant innovations in IT security products and research over the past 20 years, the information security field is still immature and struggling. Practitioners lack the ability to properly assess cyber risk, and decision-makers continue to be paralyzed by vulnerability scanners that overload their staff with mountains of scan results. In order to cope, firms prioritize vulnerability remediation using crude heuristics and limited data, though they are still too often breached by known vulnerabilities for which patches have existed for months or years. And so, the key challenge firms face is trying to identify a remediation strategy that best balances two competing forces. On one hand, it could attempt to patch all vulnerabilities on its network. While this would provide the greatest 'coverage' of vulnerabilities patched, it would inefficiently consume resources by fixing low-risk vulnerabilities. On the other hand, patching a few high-risk vulnerabilities would be highly 'efficient', but may leave the firm exposed to many other high-risk vulnerabilities. Using a large collection of multiple datasets together with machine learning techniques, we construct a series of vulnerability remediation strategies and compare how each perform in regard to trading off coverage and efficiency. We expand and improve upon the small body of literature that uses predictions of 'published exploits', by instead using 'exploits in the wild' as our outcome variable. We implement the machine learning models by classifying vulnerabilities according to high- and low-risk, where we consider high-risk vulnerabilities to be those that have been exploited in actual firm networks.
- A Jagged Little Pill: Ethics, Behavior, and the AI-Data NexusKormylo, Cameron Fredric (Virginia Tech, 2023-12-21)The proliferation of big data and the algorithms that utilize it have revolutionized the way in which individuals make decisions, interact, and live. This dissertation presents a structured analysis of behavioral ramifications of artificial intelligence (AI) and big data in contemporary society. It offers three distinct but interrelated explorations. The first chapter investigates consumer reactions to digital privacy risks under the General Data Protection Regulation (GDPR), an encompassing regulatory act in the European Union aimed at enhancing consumer privacy controls. This work highlights how consumer behavior varies substantially between high- and low-risk privacy settings. These findings challenge existing notions surrounding privacy control efficacy and suggest a more complex consumer risk assessment process. The second study shifts to an investigation of historical obstacles to consumer adherence to expert advice, specifically betrayal aversion, in financial contexts. Betrayal aversion, a well-studied phenomenon in economics literature, is defined as the strong dislike for the violation of trust norms implicit in a relationship between two parties. Through a complex simulation, it contrasts human and algorithmic financial advisors, revealing a significant decrease in betrayal aversion when human experts are replaced by algorithms. This shift indicates a transformative change in the dynamics of AI-mediated environments. The third chapter addresses nomophobia – the fear of being without one's mobile device – in the workplace, quantifying its stress-related effects and impacts on productivity. This investigation not only provides empirical evidence of nomophobia's real-world implications but also underscores the growing interdependence between technology and mental health. Overall, the dissertation integrates interdisciplinary theoretical frameworks and robust empirical methods to delineate the profound and often nuanced implications of the AI-data nexus on human behavior, underscoring the need for a deeper understanding of our relationship with evolving technological landscapes.
- Online Communities and HealthVillacis Calderon, Eduardo David (Virginia Tech, 2022-08-26)People are increasingly turning to online communities for entertainment, information, and social support, among other uses and gratifications. Online communities include traditional online social networks (OSNs) such as Facebook but also specialized online health communities (OHCs) where people go specifically to seek social support for various health conditions. OHCs have obvious health ramifications but the use of OSNs can also influence people's mental health and health behaviors. The use of online communities has been widely studied but in the health context their exploration has been more limited. Not only are online communities being extensively used for health purposes, but there is also increasing concern that the use of online communities can itself affect health. Therefore, there is a need to better understand how such technologies influence people's health and health behaviors. The research in this dissertation centers on examining how online community use influences health and health behaviors. There are three studies in this dissertation. The first study develops a conceptual model to explain the process whereby the characteristics of a request from an OHC user for social support is answered by a wounded healer, who is a person leveraging their own experiences with health challenges to help others. The second study investigates how algorithmic fairness, accountability, and transparency of an OSN newsfeed algorithm influence the users' attitudes and beliefs about childhood vaccines and ultimately their vaccine hesitancy. The third study examines how OSN social overload, through OSN use, can lead to psychological distress and received social support. The research contributes theoretical and practical insights to the literature on the use of online communities in the health context.
- Privacy Suspension with Sustainability and Trust in Consumer Adoption of Smart TechnologyChoi, Daeeun (Virginia Tech, 2022-06-09)Smart technology, such as the internet of things, artificial intelligence, and big data, provides consumers with a new level of convenience through various smart-connected products (SCPs). Although many experts have increasingly warned about the privacy vulnerability issues of various SCPs, consumers often underestimate privacy risks when adopting smart technology. Accordingly, this dissertation presents a literature review and three empirical studies that examine the privacy problems and suggest new concepts and models for a deeper understanding of the privacy suspension phenomenon. The first chapter reviews the literature related to the privacy suspension phenomenon by integrating the antecedents of consumers' privacy concerns. New concepts of privacy concerns, such as active and inactive privacy concerns, are suggested along with multiple propositions for the proposed privacy suspension theory, which extends the dimension of ambivalence toward trust and distrust regarding smart technology. The second chapter presents the proposed privacy–common good trade-off model and three assumptions related to privacy trade- offs, privacy reduction, and anchoring effects in the sustainable smart-connected car context. This study also discusses the relationships between governments, companies, and consumers regarding the effects of the common good of sustainability and government subsidies. The third chapter evaluates the mediation effects between sustainability, trust, privacy concerns, disclosure intentions, and purchase intentions when purchasing sustainable smart-connected cars based on the proposed sustainability–trust–behavior model. Finally, the fourth chapter provides a practical solution to resolve privacy suspension issues using the design science research approach. This study proposes privacy information type characteristics to evaluate SCPs' tailored data collection capabilities, visualizing them through a spider diagram design method with nudges.