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<p>The evolution of software-defined networking (SDN) has reached new heights with the advent of programmable data planes (PDP), enabling unprecedented flexibility in network design and operation. While traditional SDN solutions, constrained by protocols like OpenFlow, offered limited programmability, PDP empowers network designers to define forwarding behavior at a granular level opening new avenues for innovation in several fields such as cybersecurity. This paper presents the perspective of the STRONG Alliance, a collaborative research effort across different Ibero-America institutions, on leveraging programmable networks for advanced cybersecurity solutions. We explore the historical context of SDN, the research opportunities unlocked by PDP, and the integration of machine learning models into security countermeasures. Through our experiences and lessons learned, we aim to provide a conceptual foundation for future research in this dynamic and evolving field.</p>

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Cybersecurity Via Programmable Networks: Are We There Yet?

  • 期刊论文

  • 发表日期：

    2025年8月26日

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  • Sergio Armando Gutiérrez,
  • Juan Felipe Botero,
  • Luis Fletscher,
  • Luciano Paschoal Gaspary,
  • Natalia Gaviria,
  • Eduardo Jacob,
  • Adrian Lara,
  • Jesús Arturo Pérez-Díaz,
  • Marco Antonio To

摘要

The evolution of software-defined networking (SDN) has reached new heights with the advent of programmable data planes (PDP), enabling unprecedented flexibility in network design and operation. While traditional SDN solutions, constrained by protocols like OpenFlow, offered limited programmability, PDP empowers network designers to define forwarding behavior at a granular level opening new avenues for innovation in several fields such as cybersecurity. This paper presents the perspective of the STRONG Alliance, a collaborative research effort across different Ibero-America institutions, on leveraging programmable networks for advanced cybersecurity solutions. We explore the historical context of SDN, the research opportunities unlocked by PDP, and the integration of machine learning models into security countermeasures. Through our experiences and lessons learned, we aim to provide a conceptual foundation for future research in this dynamic and evolving field.