Identificação de tráfego malicioso em redes SDN com técnicas de inteligência computacional

• Ano de defesa: 2021
• Autor(a) principal: Boell, Victor Grudtner
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Lavras; Programa de Pós-Graduação em Ciência da Computação; UFLA; brasil; Departamento de Ciência da Computação
• Palavras-chave em Português: Rede definida por software; Redes - Segurança; Inteligência computacional; Software-defined network; Networks - Security; Computational intelligence; Ciência da Computação
• Link de acesso: http://repositorio.ufla.br/jspui/handle/1/46815

The amount of cyber attacks is increasing and becoming more and more complex. Thus, it is necessary to adopt security measures to protect computer assets. To make a decision on how to solve a problem or monitor a particular network equipment, a great deal of information is needed. This information can be attributed to data transfer points and network equipment, even with the information obtained, it is possible that the most sophisticated data go unnoticed in the eyes of more experienced administrators and security measures such as firewalls and antivirus. (SDN - Software Defined Network) technology gives you a complete view of your network. In addition, traffic processing can be done through a module external to the driver to create rules that define the paths of packets in the network. This work intends to use as characteristics of SDN technology to monitor the network in order to obtain equipment data and classify the behavior of anomalous traffic using artificial intelligence (AI - Artificial Intelligence). The result of the processing will be used to create rules for the network controller to mitigate security issues.