Cognitive security is the interception between cognitive science and artificial intelligence techniques used to protect institutions against cyberattacks. However, this field has not been addressed deeply in research. This study aims to define a Cognitive Cybersecurity Model by exploring fundamental concepts for applying cognitive sciences in cybersecurity. For achieving this, we developed exploratory research based on two steps: (1) a text mining process to identify main interest areas of research in the cybersecurity field and (2) a valuable review of the papers chosen in a systematic literature review that was carried out using PRISMA methodology. The model we propose tries to fill the gap in automatizing cognitive science without taking into account the users’ learning processes. Its definition is supported by the main findings of the literature review, as it leads to more in-depth future studies in this area.

The Generative Pre-trained Transformer (GPT) is a highly advanced natural language processing model. This model can generate conversation-style responses to user input. The rapid rise of GPT has transformed academic domains, with studies exploring the potential of chatbots in education. This research investigates the effectiveness of ChatGPT 3.5, ChatGPT 4.0 by OpenAI, and Chatbot Bing by Microsoft in solving statistical exam-type problems in the educational setting. In addition to quantifying the errors made by these chatbots, this study seeks to understand the causes of these errors to provide recommendations. A mixed-methods approach was employed to achieve this goal, including quantitative and qualitative analyses (Grounded Theory with semi-structured interviews). The quantitative stage involves statistical problem-solving exercises for undergraduate engineering students, revealing error rates based on the reason for the error, statistical fields, sub-statistics fields, and exercise types. The quantitative analysis provided crucial information necessary to proceed with the qualitative study. The qualitative stage employs semi-structured interviews with selected chatbots; this includes confrontation between them that generates agreement, disagreement, and differing viewpoints. On some occasions, chatbots tend to maintain rigid positions, lacking the ability to adapt or acknowledge errors. This inflexibility may affect their effectiveness. The findings contribute to understanding the integration of AI tools in education, offering insights for future implementations and emphasizing the need for critical evaluation and responsible use.

The growth of the Internet of Things (IoT) has accelerated digital transformation processes in organizations and cities. However, it has also opened new security challenges due to the complexity and dynamism of these systems. The application of security risk analysis methodologies used to evaluate information technology (IT) systems have their limitations to qualitatively assess the security risks in IoT systems, due to the lack of historical data and the dynamic behavior of the solutions based on the IoT. The objective of this study is to propose a methodology for developing a security risk analysis using scenarios based on the risk factors of IoT devices. In order to manage the uncertainty due to the dynamics of IoT behaviors, we propose the use of Bayesian networks in conjunction with the Best Worst Method (BWM) for multi-criteria decision-making to obtain a quantitative security risk value.

Uno de los parámetros que más influyen en la pobreza es la mala calidad en la educación. El estudio sistemático de la pobreza es fundamental para mejorar la aplicación de planes y proyectos. En el Ecuador, a partir del año del 2005 inicia el “Proyecto para mejorar las condiciones de escolaridad, el acceso y la cobertura de la educación” en zonas de alto índice de pobreza a través del Gobierno Nacional (Ministerio de Educación, 2016). Este estudio realiza un análisis espacial de dicho proyecto gubernamental del Ecuador mediante el uso del software libre. Dicho análisis se fundamenta en la existencia de las instituciones educativas públicas denominadas “Unidades Educativas del Milenio” (UEM), cuyo fin es mejorar la calidad académica, satisfacer la demanda estudiantil rural y atender a sectores históricamente relegados, partiendo de 57 unidades educativas operativas en el año 2016 y utilizando técnicas de análisis espacial estadístico, apoyados en una base de datos relacional robusta como es el caso de PostgreSQL, con el fin de determinar cuál es su área de afectación a la población, creando varios tipos de coberturas para identificar las parroquias y el porcentaje de pobreza que es atendido por este proyecto educacional, logrando determinar que existe un 77% y el 96% de UEM, en zonas de extrema pobreza.