Santiago Sánchez-Viteri

The journal retracts the article, Development of a Platform for Learning Cybersecurity Using Capturing the Flag Competitions

In the era of Industry 4.0, mastering the management and analysis of large volumes of data has become essential to foster technological advances and improve operations. Our work presents a methodology combining spatial and temporal data with deep learning techniques, specifically Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory and Gated Recurrent Units, and Deep Neural Networks. We aim to transform anomaly detection, perform predictive maintenance, and optimize industrial processes. Our work has found that intelligent integration of multiple data sources improves accuracy and other key indicators, such as F1 score and AUC, and enriches decision-making with more profound, detailed information about the operating environment. The results have been promising. We have seen an increase in anomaly detection accuracy by up to 92%, an improvement in early detection for predictive maintenance by 150%, and an improvement in operational efficiency from 70% to 85%. These advances validate our proposal and demonstrate its practicality in various industrial environments. This work proposes a guide for integrating data fusion technologies in Industry 4.0, highlighting the practical benefits of our methodology and opening new possibilities for innovation and improving operational efficiency.

This work addresses assessing air quality and noise in urban environments by integrating predictive models and Internet of Things technologies. For this, a model generated heat maps for PM2.5 and noise levels, incorporating traffic data from open sources for precise contextualization. This approach reveals significant correlations between high pollutant/noise concentrations and their proximity to industrial zones and traffic routes. The predictive models, including convolutional neural networks and decision trees, demonstrated high accuracy in predicting pollution and noise levels, with correlation values such as R2 of 0.93 for PM2.5 and 0.90 for noise. These findings highlight the need to address environmental issues in urban planning comprehensively. Furthermore, the study suggests policies based on the quantitative results, such as implementing low-emission zones and promoting green spaces, to improve urban environmental management. This analysis offers a significant contribution to scientific understanding and practical applicability in the planning and management of urban environments, emphasizing the relevance of an integrated and data-driven approach to inform effective policy decisions in urban environmental management.

Currently, the internet of things (IoT) is a technology entering various areas of society, such as transportation, agriculture, homes, smart buildings, power grids, etc. The internet of things has a wide variety of devices connected to the network, which can saturate the central links to cloud computing servers. IoT applications that are sensitive to response time are affected by the distance that data is sent to be processed for actions and results. This work aims to create a prototype application focused on emergency vehicles through a fog computing infrastructure. This technology makes it possible to reduce response times and send only the necessary data to cloud computing. The emergency vehicle contains a wireless device that sends periodic alert messages, known as an in-vehicle beacon. Beacon messages can be used to enable green traffic lights toward the destination. The prototype contains fog computing nodes interconnected as close to the vehicle as using the low-power whole area network protocol called a long-range wide area network. In the same way, fog computing nodes run a graphical user interface (GUI) application to manage the nodes. In addition, a comparison is made between fog computing and cloud computing, considering the response time of these technologies.

Currently, cybersecurity is a topic of great importance for society. With the increase in the use of technology and the digitization of many activities, the number of cyber threats to which individuals and organizations are exposed has increased. In addition, the COVID-19 pandemic has accelerated the digitization of many processes, further increasing the risk of cyberattacks. One of the main causes of these problems is the lack of cyber security awareness, as many people and organizations do not have a proper understanding of cyber threats and the measures, they must take to protect themselves. As a solution to the lack of cybersecurity knowledge, this work proposes the development of a Capture the Flag platform for learning about cybersecurity. The objective is to provide a tool that allows the education of future professionals in this field and covers the existing demand for this type of specialist. The platform is made up of two sections, one for learning and the other for CTF. The first section allows teachers to contribute to the teaching of their students using challenges. The second section allows one to carry out competitions with effective results when acquiring knowledge and experience. The platform is evaluated using questionnaires and surveys to measure whether the platform fulfills its purpose.

The inclusion of information and communication technologies in education has become a priority for all universities. To meet this need, there are several research works that have dealt with the subject for several decades. However, for its inclusion, the analysis of each institution is necessary since the needs of the university population and the resources for its application change according to each situation. This work seeks to create a method that allows establishing the needs and doubts of students about the use of educational technologies in the classroom without affecting their performance. For this, a process has been designed that identifies learning needs, through the validation of data obtained from surveys and the monitoring of the academic efficiency and learning of a cohort of students. The follow-up includes a period of four years from 2019 to 2022. This follow-up allowed establishing three different realities, in 2019 the academic data was analyzed in a face-to-face education model, from 2020 to 2021 the follow-up was included in a remote model with the use of technologies as a communication channel and in 2022 these were included as a learning component, which marked an in-depth analysis of student performance and how technology affected their learning.

The inclusion of information and communication technologies in education has become a priority for all educational models, particularly for higher education institutes that have observed the need to integrate these technologies in the classroom. However, to guarantee educational quality and learning, establishing a process that allows the identification of the response of the students towards its use is necessary. For this purpose, there are several works that address the issue and have determined the functionality of these technologies, but each environment is different, and this is recognized by the higher education institutes of Ecuador that have limited economic, technological, and academic resources. This work seeks to create a method that allows the needs and doubts of students about the use of educational technologies in the classroom to be established without affecting their academic performance. To perform this, a process has been designed that identifies learning needs through the validation of data obtained from surveys and a comparison of two groups of students, in which one group makes use of technologies in the classroom and the other group uses a model of traditional education. By obtaining the results of the analysis, the method determines the impact of technology on learning.

The pandemic caused by the 2019 coronavirus disease has marked a total change in the development of society. Since then, its effects have been visible in people, both in work, education and psychological areas. There are many jobs and organizations that have set out to identify the reality of people after the pandemic and how the pandemic has affected their daily lives. To do this, countries have organized data and statistics collection campaigns that allow investigating the new needs of people. With this, instruments such as surveys have become more relevant and valid to know what these needs are. However, the analysis processes must guarantee answers that are able to determine the direct impact that each question has on people’s feelings. This work proposes a framework to determine the incidence values of surveys based on their categories and questions and how they capture the reality of people in areas such as education, the impact of work, family and the stress generated by the pandemic. With the results obtained, each element and category that the population considers a consequence of COVID-19 that affects the normal development of life has been identified.

Currently, society is going through a health event with devastating results. In their desire to control the 2019 coronavirus disease, large organizations have turned over the execution of their activities to the use of information technology. These tools, adapted to the use of the Internet, have been presented as an effective solution to the measures implemented by the majority of nations where quarantines are generalized. However, the solution given by information technologies has several disadvantages that must be solved. The most important in this regard is with the serious security incidents that exist, where many organizations have been compromised and their data has been exposed. As a solution, this work proposes the design of a guide that allows for the implementation of a computer incident response team on a university campus. Universities are optimal environments for the generation of new technologies; they also serve as the ideal test bed for the generation of security policies and new treatments for incidents in an organization. In addition, with the implementation of the computer incident response team in a university, it is proposed to be part of a response group to any security incident at the national level.

Currently, education is going through a critical moment due to the 2019 coronavirus disease that has been declared a pandemic. This has forced many organizations to undergo a significant transformation, rethinking key elements of their processes and the use of technology to maintain operations. The continuity of education has become dependent on technological tools, as well as on the ability of universities to cope with a precipitous transition to a remote educational model. That has generated problems that affect student learning. This work proposes the implementation of a Big Data framework to identify the factors that affect student performance and decision-making to improve learning. Similar works cover two main research topics under Big Data in education, the modeling and storage of educational data. However, they do not consider issues such as student performance and the improvement of the educational system with the integration of Big Data. In addition, this work provides a guide for future studies and highlights new insights and directions for the successful use of Big Data in education. Real-world data were collected for the evaluation of the proposed framework, the collection of these being the existing limitation in all research due to generalized rejection of data consent.