La inteligencia artificial en la detección de intrusiones en entornos de redes definidas por software (SDN).

Software Defined Networking (SDN) represents an innovation in network management by providing significant flexibility and efficiency in its operation. However, this flexibility and efficiency also brings with it significant security challenges. The steady increase in cyber threats has become a serious concern, especially due to the centralized architecture of SDN, which relies on logical controllers to monitor data flows throughout the network. The proposed solution includes the application of artificial intelligence (AI) in attack mitigation, leveraging the ability to analyze data and detect anomalous patterns in real time, this involves proactively identifying threats and vulnerabilities in SDN environments. This is justified by the need to address emerging threats in SDN and ensure network security and service availability. This study is based on a literature review and highlights the importance of machine learning algorithms, to improve intrusion detection in SDN environments. Implementing AI in SDN security goes beyond detecting threats and effectively protecting network resources and services. This is a key element to ensure the resilience and reliability of SDN in a constantly evolving cyber threat e environment.

• Hovedforfatter: Zambrano Moran, Janeth Lissette (author)
• Format: bachelorThesis
• Udgivet: 2023
• Fag: SDN; Inteligencia Artificial; Detección de Intrusiones; Ciberseguridad; Aprendizaje Automático; Arquitectura SDN; Aprendizaje profundo; Controlador SDN; Vulnerabilidades de SDN; OpenFlow; Centralización de Control
• Online adgang: http://dspace.utb.edu.ec/handle/49000/15119