The objective of this research was to propose a conceptual cybersecurity framework aimed at guiding developers in generating and implementing technological solutions for Edge Computing and Internet of Things (IoT) environments. The framework integrates NIST standards and SecDevOps practices, and was developed based on an extensive literature review, synthesizing evidence-based knowledge to offer a comprehensive perspective on actions necessary to address cybersecurity challenges in these environments. The core element of the framework, Govern, led to four primary components: Identity, Protect, Detect, and Respond and Recover. Each component outlines specific actions for identifying cybersecurity vulnerabilities, implementing strategies, and prioritizing privacy and integrity requirements. In order to establish a solid theoretical foundation of the proposal, the framework was conceptually validated through a qualitative method for collecting feedback from a panel of 35 experts from industry, government, and academia. Evaluators confirmed the framework’s relevance, highlighting its integration of NIST standards and SecDevOps practices. This combination is regarded as offering a modular and effective approach for aligning cybersecurity practices with governance principles, addressing cybersecurity challenges, enhancing compliance readiness, supporting secure development, and fostering resilient architectures in IoT and Edge Computing environments. The findings of this evaluation are perceived as promising, since the proposal is considered potentially beneficial to the field of cybersecurity by providing a structured practical framework that could serve as a foundational tool for strengthening security practices in Edge Computing and IoT environments.

Este artículo presenta un modelo de análisis de datos en Internet basado en Minería Web con el objetivo de encontrar conocimiento sobre grandes cantidades de datos en el ciberespacio. A fin de probar el método propuesto, se analizaron páginas web sobre el suicidio como caso de estudio con la intención de identificar y detectar rasgos en estudiantes con tendencias suicidas. El procedimiento considera un Web Scraper para localizar y descargar información de Internet, así como técnicas de Procesamiento de Lenguaje Natural para la recuperación de los vocablos. Con el propósito de explorar la información, se construyó un conjunto de datos basado en Tablas Dinámicas y Ontologías Semánticas, especificando las variables predictivas en jóvenes con inclinación suicida. Por último, para evaluar la eficiencia del modelo se utilizaron algoritmos de Aprendizaje de Máquina y Aprendizaje Profundo. Cabe destacar que se optimizaron los procedimientos para la construcción del dataset (utilizando Algoritmos Genéticos) y obtención del conocimiento empleando Cómputo Paralelo y Aceleración con Unidades de Procesamientos de Gráfico (GPU). Los resultados revelan una precisión del 96,28% sobre la detección de las características en adolescentes con tendencia suicida, alcanzando el mejor resultado a través de una Red Neuronal Recurrente con un 98% de precisión. De donde se infiere que el modelo es viable para establecer bases sobre mecanismos de actuación y prevención de comportamientos suicidas, que pueden ser implementados en instituciones educativas o distintos actores de la sociedad. This article presents an Internet data analysis model based on Web Mining with the aim to find knowledge about large amounts of data in cyberspace. To test the proposed method, suicide web pages were analyzed as a study case to identify and detect traits in students with suicidal tendencies. The procedure considers a Web Scraper to locate and download information from the Internet, as well as Natural Language Processing techniques to retrieve the words. To explore the information, a dataset based on Dynamic Tables and Semantic Ontologies was constructed, specifying the predictive variables in young people with suicidal inclination. Finally, to evaluate the efficiency of the model, Machine Learning and Deep Learning algorithms were used. It should be noticed that the procedures for the construction of the dataset (using Genetic Algorithms) and obtaining the knowledge (using Parallel Computing and Acceleration with GPU) were optimized. The results reveal an accuracy of 96.28% on the detection of characteristics in adolescents with suicidal tendencies, reaching the best result through a Recurrent Neural Network with 98% accuracy. It is inferred that the model is viable to establish bases on mechanisms of action and prevention of suicidal behaviors, which can be implemented in educational institutions or different social actors.

The objective of this article was to establish the first contextual approaches for the development of a framework focused on the design of automotive infotainment systems. Therefore, a method was developed to identify a group of characteristics of the infotainment system interfaces that ensure a good user experience by the user, according to previous studies. Besides, the establishment of usability is proposed as a basis for the development of the tool.

We evaluated logistic regression as a classifier in the diagnosis of breast cancer based on Raman spectra. Common studies published in the subject use dimensional reduction techniques to generate the classifier. Instead, we proposed to observe the effect of using all intensity values recorded in the spectra as input variables to the algorithm. We used leaving one out cross-validation measuring classification accuracy, sensitivity and specificity. We used Raman spectra taken from breast tissue previously diagnosed by histopathological analysis, some from healthy tissue and some from tissue with cancer. Each spectrum is formed by 605 intensity values in the range of 687 to 1781 cm-1. Logistic regression classifier exhibited 100% classification accuracy. To establish comparative references, we evaluated in the same way: 1) a logistic model preceded by dimensional reduction with Principal Component Analysis (PCA+LR), 2) two classifiers obtained with weighted K nearest neighbors algorithm, and 3) a classifier using the naive Bayes (NB) algorithm. We found that PCA+LR and NB showed the same performance of 100% in classification accuracy. Nevertheless, PCA+LR requires more processing computational time.