<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0123-3033</journal-id>
<journal-title><![CDATA[Ingeniería y competitividad]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. compet.]]></abbrev-journal-title>
<issn>0123-3033</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Ingeniería, Universidad del Valle]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0123-30332022000200024</article-id>
<article-id pub-id-type="doi">10.25100/iyc.v24i2.11762</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[An adaptable Intelligence Algorithm to a Cybersecurity Framework for IIOT]]></article-title>
<article-title xml:lang="es"><![CDATA[Un algoritmo de inteligencia adaptable a un marco de ciberseguridad para IIOT]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ordoñez-Tumbo]]></surname>
<given-names><![CDATA[Santiago]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Márceles-Villalba]]></surname>
<given-names><![CDATA[Katerine]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Amador-Donado]]></surname>
<given-names><![CDATA[Siler]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Institución Universitaria Colegio Mayor del Cauca Faculty of Engineering ]]></institution>
<addr-line><![CDATA[Popayán ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad del Cauca Faculty of Electronic Engineering and Telecommunicatios ]]></institution>
<addr-line><![CDATA[Popayán ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>24</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0123-30332022000200024&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0123-30332022000200024&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0123-30332022000200024&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The industrial internet of things (IIoT) has grown in recent years, which makes it possible to publicize recent technological innovations and be able to integrate them with each other, such as smart cities, among other applications such as health, education, transit and others, but at the same time there is a problem that is security, due to the fact that incidents related to IIoT have been registered against data networks, for this reason it is necessary to generate intelligent solutions in cybersecurity, which allow to give a satisfactory solution. The objective of this work was to propose an intelligence technique adaptable to a cybersecurity framework with the ability to solve cybersecurity problems in networks of IIoT devices, for the development of which the research-action methodology (I-A), which consists of merging theory with practice in such a way that the researcher can generate accurate conclusions about the practices carried out. In this sense, with this methodology it is intended to provide solutions to specific problems in a given situation. Based on the above, a systematic literature review of the different artificial intelligence techniques was carried out, to finally determine the most appropriate ones and proceed to carry out the respective validations until the appropriate one was selected. Where it was found that there is a great variety of intelligence techniques such as Deep Learning (Deep Learning), who obtained a very high score in the characterization that was carried out due to its great possibilities when integrating the algorithm into the field of cybersecurity, it was identified that they are very poorly characterized; however, in the initial research that was done, the result was how to work with this technology and how to adapt it to cybersecurity. There are different ways to analyze and secure data on the network, one of these is learning techniques, in this research several techniques were identified that with their respective algorithms provided the basis for adaptability with a framework related to IIoT technologies.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El internet industrial de las cosas (IIoT) ha tenido un crecimiento en los últimos años, que permite dar a conocer las recientes innovaciones tecnológicas y poder integrarlas entre sí, como lo son las ciudades inteligentes, entre otras aplicaciones como la salud, educación, tránsito y otras más, pero a su vez se cuenta con una problemática que es la seguridad, debido a que se han registrado incidentes relacionados con IIoT frente a las redes de datos, por ello se hace necesario generar soluciones inteligentes en ciberseguridad, que permitan dar una solución satisfactoria. El objetivo de este trabajo fue proponer una técnica de inteligencia adaptable a un framework de ciberseguridad con la capacidad de solucionar los problemas de ciberseguridad en las redes de los dispositivos IIoT, para el desarrollo de éste se hace uso de la metodología investigación-acción (I-A), la cual consiste en fusionar la teoría con la práctica de tal forma que el investigador pueda generar conclusiones acertadas sobre las prácticas realizadas. En este mismo sentido, con dicha metodología se pretende dar soluciones a problemas concretos en una situación determinada. A partir de lo anterior, se realizó una revisión sistemática de literatura de las diferentes técnicas de inteligencia artificial, para finalmente determinar las más adecuadas y proceder hacer las respectivas validaciones hasta seleccionar la apropiada. Donde se encontró que existen una gran variedad de técnicas de inteligencia como Deep Learning (aprendizaje profundo), quien obtuvo un puntaje muy alto en la caracterización que se realizó por sus grandes posibilidades a la hora de integrar el algoritmo al ámbito de la ciberseguridad, se identificó que se encuentran muy poco caracterizados; sin embargo, en la investigación inicial que se hizo, se obtuvo como resultado el cómo trabajar con esta tecnología y cómo poderla adaptar a la ciberseguridad. Existen diferentes formas de analizar y dar seguridad a los datos en la red, una de esas son las técnicas de aprendizaje, en esta investigación se identificaron varias técnicas que con sus respectivos algoritmos proporcionaron las bases para la adaptabilidad con un framework relacionado con tecnologías IIoT.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Cybersecurity]]></kwd>
<kwd lng="en"><![CDATA[Industrial Internet of Things-IIoT]]></kwd>
<kwd lng="en"><![CDATA[Artificial Intelligence]]></kwd>
<kwd lng="en"><![CDATA[Intrusion Detection System]]></kwd>
<kwd lng="en"><![CDATA[Security Models]]></kwd>
<kwd lng="es"><![CDATA[Ciberseguridad]]></kwd>
<kwd lng="es"><![CDATA[Internet industrial de las cosas-IIoT]]></kwd>
<kwd lng="es"><![CDATA[Inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[algoritmos de ML]]></kwd>
<kwd lng="es"><![CDATA[Modelos de seguridad]]></kwd>
</kwd-group>
</article-meta>
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