Vargas Rosales, CésarYungaicela Naula, Noé Marcelo2023-09-192023-09-192023-06-01Yungaicela Naula, N. M. (2023). Security automation in software defined networks [Tesis Doctorado]. Instituto Tecnológico y de Estudios Superiores de Monterrey. Recuperado de: https://hdl.handle.net/11285/651165https://hdl.handle.net/11285/651165https://orcid.org/0000-0003-1770-471XThe exponential increase of devices connected to the internet, and the conventional networking operation, based on distributed and static network management, have made networking an incredibly complex task. Software-Defined Networking (SDN) solves the problems arising from the static nature of conventional networking by introducing dynamism to the networking operation. SDN separates the data plane and control plane, centralizes the network control, and automates the network management. In particular, SDN technology is an effective solution to provide security to different network environments. This study solves the security problem in SDN-based networks using state-of-the-art artificial intelligent (AI) techniques. An automated security framework is proposed which integrates two components: 1) Reactive, and 2) Proactive parts. The reactive component uses Deep Learning (DL) to identify complex DDoS threats and Reinforcement Learning (RL) to mitigate them. The proactive component leverages Network Function Virtualization (NFV) to provide scalability to the proposed security framework. Extensive experiments using datasets, simulations, and physical deployments demonstrate the effectiveness of the proposed security automation framework.TextoengopenAccesshttp://creativecommons.org/licenses/by/4.0INGENIERÍA Y TECNOLOGÍA::CIENCIAS TECNOLÓGICAS::TECNOLOGÍA DE LOS ORDENADORES::INTELIGENCIA ARTIFICIALTechnologyTesis de doctoradoEl documento de tesis contiene información de artículos publicados que requieren embargo de dos años a su fecha de publicación. El último artículo incluido en el documento se estima que será publicado a más tardar a finales del año 2023, en la revista Future Generation Computer Systems (Online ISSN: 1872-7115) que tiene un periodo de embargo de 24 meses.https://orcid.org/0000-0002-3131-0672Machine learningDeep learningReinforcement learningNetwork securitySoftware defined networkDDoS attacksSlow-rate DDoS attacks78129157203986401