Resumen

ORDONEZ-TUMBO, Santiago; MARCELES-VILLALBA, Katerine  y  AMADOR-DONADO, Siler. An adaptable Intelligence Algorithm to a Cybersecurity Framework for IIOT. Ing. compet. [online]. 2022, vol.24, n.2, e22511762.  Epub 26-Mayo-2022. ISSN 0123-3033.  https://doi.org/10.25100/iyc.v24i2.11762.

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.

Palabras clave : Cybersecurity; Industrial Internet of Things-IIoT; Artificial Intelligence; Intrusion Detection System; Security Models.

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