Una revisión sistemática integral de las redes neuronales y su impacto en la detección de sitios web maliciosos en los usuarios de la red

The large branches of Machine Learning represent an immense support for the detection of malicious websites, they can predict whether a URL is malicious or benign, leaving aside the cyber attacks that can generate for network users who are unaware of them. The objective of the research was to know the state of the art about Neural Networks and their impact for the Detection of malicious Websites in network users. For this purpose, a systematic literature review (SLR) was conducted from 2017 to 2021. The search identified 561 963 papers from different sources such as Taylor & Francis Online, IEEE Xplore, ARDI, ScienceDirect, Wiley Online Library, ACM Digital Library and Microsoft Academic. Of the papers only 82 were considered based on exclusion criteria formulated by the author. As a result of the SLR, studies focused on machine learning (ML), where it recommends the use of algorithms to have a better and efficient prediction of malicious websites. For the researchers, this review presents a mapping of the findings on the most used machine learning techniques for malicious website detection, which are essential for a study because they increase the accuracy of an algorithm. It also shows the main machine learning methodologies that are used in the research papers.as grandes ramas de Machine Learning representan un inmenso apoyo para la detección de sitios web maliciosos, pueden predecir si una URL es maliciosa o benigna, dejando de lado los ciberataques que pueden generar para los usuarios de la red que los desconozcan. El objetivo de la investigación fue conocer el estado del arte sobre las Redes Neuronales y su impacto para la Detección de Sitios Web maliciosos en los usuarios de la red. Para ello, se realizó una revisión sistemática de la literatura (SLR) de 2017 a 2021. La búsqueda identificó 561 963 artículos de diferentes fuentes como Taylor & Francis Online, IEEE Xplore, ARDI, ScienceDirect, Wiley Online Library, ACM Digital Library y Microsoft Académico. De los artículos solo 82 fueron considerados en base a los criterios de exclusión formulados por el autor. Como resultado de SLR, los estudios se centraron en el aprendizaje automático (ML), donde recomienda el uso de algoritmos para tener una mejor y eficiente predicción de sitios web maliciosos. Para los investigadores, esta revisión presenta un mapeo de los hallazgos sobre las técnicas de aprendizaje automático más utilizadas para la detección de sitios web maliciosos, que son esenciales para un estudio porque aumentan la precisión de un algoritmo. También muestra las principales metodologías de aprendizaje automático que se utilizan en los trabajos de investigación

A Comprehensive Systematic Review of Neural Networks and Their Impact on the Detection of Malicious Websites in Network Users

The large branches of Machine Learning represent an immense support for the detection of malicious websites, they can predict whether a URL is malicious or benign, leaving aside the cyber attacks that can generate for network users who are unaware of them. The objective of the research was to know the state of the art about Neural Networks and their impact for the Detection of malicious Websites in network users. For this purpose, a systematic literature review (SLR) was conducted from 2017 to 2021. The search identified 561 963 papers from different sources such as Taylor & Francis Online, IEEE Xplore, ARDI, ScienceDirect, Wiley Online Library, ACM Digital Library and Microsoft Academic. Of the papers only 82 were considered based on exclusion criteria formulated by the author. As a result of the SLR, studies focused on machine learning (ML), where it recommends the use of algorithms to have a better and efficient prediction of malicious websites. For the researchers, this review presents a mapping of the findings on the most used machine learning techniques for malicious website detection, which are essential for a study because they increase the accuracy of an algorithm. It also shows the main machine learning methodologies that are used in the research papers

Divulgación Científica No.2

La Universidad del Rosario lidera, con acierto, diferentes temas de investigación desde los enfoques de cada una de las disciplinas que conforman la universidad, con miradas que buscan integrar a la discusión lo transdisciplinar y multidisciplinar. Los saberes que circulan por cada una de sus unidades académicas constituyen el punto de partida para asumir las preguntas que motivan cada uno de los frentes de investigación, dar respuestas a los problemas que nos aquejan y proponer nuevas rutas para trasegar como sociedad. Conscientes de la necesidad de un diálogo constante y abierto con amplios sectores que garanticen un público diverso y múltiple, la Universidad del Rosario presenta el segundo número de Divulgación Científica, que al igual que el primero, cuenta, por medio del periodismo científico, los retos y las posibilidades que como institución nos planteamos para pensar nuestro mundo.The Universidad del Rosario successfully leads different research topics from the approaches of each of the disciplines that make up the university, with views that seek to integrate the transdisciplinary and multidisciplinary discussion. The knowledge that circulates through each of its academic units constitutes the starting point to assume the questions that motivate each of the research fronts, provide answers to the problems that afflict us and propose new routes to move as a society. Aware of the need for a constant and open dialogue with broad sectors that guarantee a diverse and multiple audience, the Universidad del Rosario presents the second issue of Scientific Dissemination, which, like the first, tells, through scientific journalism, the challenges and the possibilities that as an institution we consider to think about our world

Divulgación Científica No.2

La Universidad del Rosario lidera, con acierto, diferentes temas de investigación desde los enfoques de cada una de las disciplinas que conforman la universidad, con miradas que buscan integrar a la discusión lo transdisciplinar y multidisciplinar. Los saberes que circulan por cada una de sus unidades académicas constituyen el punto de partida para asumir las preguntas que motivan cada uno de los frentes de investigación, dar respuestas a los problemas que nos aquejan y proponer nuevas rutas para trasegar como sociedad. Conscientes de la necesidad de un diálogo constante y abierto con amplios sectores que garanticen un público diverso y múltiple, la Universidad del Rosario presenta el segundo número de Divulgación Científica, que al igual que el primero, cuenta, por medio del periodismo científico, los retos y las posibilidades que como institución nos planteamos para pensar nuestro mundo.The Universidad del Rosario successfully leads different research topics from the approaches of each of the disciplines that make up the university, with views that seek to integrate the transdisciplinary and multidisciplinary discussion. The knowledge that circulates through each of its academic units constitutes the starting point to assume the questions that motivate each of the research fronts, provide answers to the problems that afflict us and propose new routes to move as a society. Aware of the need for a constant and open dialogue with broad sectors that guarantee a diverse and multiple audience, the Universidad del Rosario presents the second issue of Scientific Dissemination, which, like the first, tells, through scientific journalism, the challenges and the possibilities that as an institution we consider to think about our world

Diminishing benefits of urban living for children and adolescents’ growth and development

Optimal growth and development in childhood and adolescence is crucial for lifelong health and well-being1–6. Here we used data from 2,325 population-based studies, with measurements of height and weight from 71 million participants, to report the height and body-mass index (BMI) of children and adolescents aged 5–19 years on the basis of rural and urban place of residence in 200 countries and territories from 1990 to 2020. In 1990, children and adolescents residing in cities were taller than their rural counterparts in all but a few high-income countries. By 2020, the urban height advantage became smaller in most countries, and in many high-income western countries it reversed into a small urban-based disadvantage. The exception was for boys in most countries in sub-Saharan Africa and in some countries in Oceania, south Asia and the region of central Asia, Middle East and north Africa. In these countries, successive cohorts of boys from rural places either did not gain height or possibly became shorter, and hence fell further behind their urban peers. The difference between the age-standardized mean BMI of children in urban and rural areas was <1.1 kg m–2 in the vast majority of countries. Within this small range, BMI increased slightly more in cities than in rural areas, except in south Asia, sub-Saharan Africa and some countries in central and eastern Europe. Our results show that in much of the world, the growth and developmental advantages of living in cities have diminished in the twenty-first century, whereas in much of sub-Saharan Africa they have amplified