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Este estudio presenta un marco de aprendizaje automático para la detección de ataques de denegación de servicio distribuido (DDoS) en redes definidas por software (SDN). Un modelo híbrido Random Forest-XGBoost logró una precisión del 99,36 %, ofreciendo una detección temprana confiable para redes programables.

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Área de la Ciencia:

  • Informática
  • Seguridad de redes
  • Aprendizaje automático

Sus antecedentes:

  • Las redes definidas por software (SDN) ofrecen programabilidad pero introducen vulnerabilidades a ataques como la denegación de servicio distribuida (DDoS).
  • Los métodos de detección existentes a menudo carecen de especificidad para entornos SDN, lo que requiere características de tráfico conscientes de SDN.
  • Las redes basadas en OpenFlow requieren enfoques adaptados para una identificación eficaz de amenazas.

Objetivo del estudio:

  • Desarrollar y evaluar un marco de aprendizaje automático para la detección temprana de ataques DDoS en SDN.
  • Diseñar nuevas características de tráfico específicas de SDN para mejorar la identificación de amenazas.
  • Evaluar el rendimiento de un modelo de clasificación híbrido Random Forest (RF) y XGBoost (XGB).

Principales métodos:

  • Se construyó un conjunto de datos a partir de un banco de pruebas SDN utilizando un controlador Ryu y Open vSwitch.
  • Se recopilaron estadísticas a nivel de flujo y puerto a través de mensajes de monitoreo OpenFlow.
  • Se diseñaron características específicas de SDN y se desarrolló un modelo de clasificación híbrido RF-XGB.

Principales resultados:

  • El modelo híbrido RF-XGB logró una precisión del 99,36 % en la distinción entre tráfico benigno y malicioso.
  • Demostró un rendimiento superior en comparación con los clasificadores individuales Random Forest y XGBoost.
  • Exhibió una discriminación casi perfecta en las evaluaciones del área bajo la curva (AUC) de la característica operativa del receptor (ROC) y la matriz de confusión.

Conclusiones:

  • La combinación de la ingeniería de características específicas de SDN con el aprendizaje de conjuntos (RF-XGB) es muy eficaz para la detección temprana de DDoS.
  • El marco propuesto ofrece una solución confiable para mejorar la seguridad en redes programables.
  • Las características conscientes de SDN son cruciales para identificar con precisión amenazas de red sofisticadas.