<?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>2960-2467</journal-id>
<journal-title><![CDATA[e-Revista Multidisciplinaria del Saber]]></journal-title>
<abbrev-journal-title><![CDATA[e-Rev. Multidiscip. Saber]]></abbrev-journal-title>
<issn>2960-2467</issn>
<publisher>
<publisher-name><![CDATA[Centro de Investigación en Tecnologías de Salud y Ambiente (CITSA)]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2960-24672025000100264</article-id>
<article-id pub-id-type="doi">10.61286/e-rms.v3i.286</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Virtualización de funciones de red y blockchain con IA para servicios de red seguros y escalables]]></article-title>
<article-title xml:lang="en"><![CDATA[AI-blockchain and Network function virtualization for secure and scalable network services]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Abdulkadhum]]></surname>
<given-names><![CDATA[Ahmed Mahdi]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Al-Qasim Green University  ]]></institution>
<addr-line><![CDATA[Babylon ]]></addr-line>
<country>Iraq</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<volume>3</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_arttext&amp;pid=S2960-24672025000100264&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_abstract&amp;pid=S2960-24672025000100264&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_pdf&amp;pid=S2960-24672025000100264&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Esto se logra empleando sistemas de detección de intrusiones basados en el aprendizaje profundo, escalado automático asistido por aprendizaje por refuerzo, y asignación predictiva de tareas virtuales para garantizar la rendición de cuentas y la eficiencia del servicio. Adicionalmente, Blockchain se utiliza para proporcionar mecanismos de auditoría transparentes, asegurar el cumplimiento de los Acuerdos de Nivel de Servicio, y ofrecer evidencia a prueba de manipulaciones mediante contratos inteligentes y oráculos. Los resultados revelan una ventaja notable sobre los sistemas convencionales: la precisión de la detección aumentó del 91,4% al 97,9% al combinar la Inteligencia Artificial y Blockchain, mientras que la tasa de falsas alarmas se redujo en más del 50%. Las latencias también disminuyeron en más del 30%, la adhesión a los Acuerdos de Nivel de Servicio mejoró del 85,3% al 96,5%, y el consumo energético se redujo en aproximadamente un 32%. El modelo híbrido alcanzó la tasa de confianza más alta (0,94), lo que demuestra que fusionar la Inteligencia Artificial con blockchain no solo mejora el rendimiento técnico, sino que también garantiza el cumplimiento normativo y la fiabilidad operativa.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract As the demand for a high-performance, efficient, and secure data center operation level intensifies, traditional network architectures prove insufficient for contemporary digital environments. The emerging application of scientific knowledge for practical purposes, such as cloud computing, Artificial Intelligence, the Internet of Things, and Big Data, has overburdened existing infrastructures, driving the need for advanced solutions. In this paper, a new architecture is designed that merges security and intelligence by integrating the features of Network Function Virtualization, Artificial Intelligence, and Blockchain technology. The main objective is to achieve high levels of security, reliability, and scalability in emerging networks. This is accomplished by employing intrusion detection systems based on deep learning, automatic scaling assisted by reinforcement learning, and predictive allocation of virtual tasks to ensure accountability and service efficiency. Additionally, Blockchain is utilized to provide transparent auditing mechanisms, ensure compliance with Service Level Agreements, and offer tamper-proof evidence through smart contracts and oracles. The results reveal a notable advantage over conventional systems: detection accuracy increased from 91.4% to 97.9% when combining Artificial Intelligence and Blockchain, while the false alarm rate was reduced by more than 50%. Latencies also decreased by more than 30%, Service Level Agreement adherence improved from 85.3% to 96.5%, and energy consumption was reduced by approximately 32%. The hybrid model achieved the highest trust rate (0.94), demonstrating that merging Artificial Intelligence with blockchain not only enhances technical performance but also guarantees regulatory compliance and operational reliability.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Artificial Intelligence]]></kwd>
<kwd lng="en"><![CDATA[Network Function Virtualization]]></kwd>
<kwd lng="en"><![CDATA[Blockchain]]></kwd>
<kwd lng="en"><![CDATA[Service Level Agreement]]></kwd>
<kwd lng="en"><![CDATA[Explainable Artificial Intelligence]]></kwd>
<kwd lng="en"><![CDATA[Artificial Intelligence-based Intrusion Detection System]]></kwd>
<kwd lng="es"><![CDATA[Inteligencia Artificial]]></kwd>
<kwd lng="es"><![CDATA[Virtualización de Funciones de Red]]></kwd>
<kwd lng="es"><![CDATA[Blockchain]]></kwd>
<kwd lng="es"><![CDATA[Acuerdo de Nivel de Servicio]]></kwd>
<kwd lng="es"><![CDATA[Inteligencia Artificial Explicable]]></kwd>
<kwd lng="es"><![CDATA[Sistema de Detección de Intrusiones basado en Inteligencia Artificial]]></kwd>
</kwd-group>
</article-meta>
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