Proposta de um sistema de detecção e classificação de intrusão em redes de computadores baseado em transformadas wavelets e redes neurais artificiais

• Ano de defesa: 2009
• Autor(a) principal: Ferreira, Ed' Wilson Tavares
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; BR; Programa de Pós-graduação em Engenharia Elétrica; Engenharias; UFU
• Palavras-chave em Português: Sistema de detecção de intrusão IDS; Transformadas wavelets; Redes neurais artificiais; Aprendizagem por quantização vetorial; Segurança em redes de computadores; Redes de computação - Medidas de segurança; Intrusion detections systems (IDS); Wavelets; Neural artificial networks; Learning vector quantization; Network security; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/14262

As the Internet has become an enormous interconnected network, the information security today is very important to guarantee confidentiality, integrity and availability of computing resources. Advanced Intrusion Detections Systems (IDS) should be capable of identifying malicious actions that may compromise these guarantees, as quickly as possible. In this work, we present a hybrid approach for the IDS, with two different techniques: wavelets and artificial neural network. The wavelet is used to indicate to detect anomalous behavior on the network, while the neural networks are used to classify the attacks. A prototype was developed and evaluated data from simulation tests, on laboratory network and the KDD99 database. Besides the analysis of results of the other approaches, was also carried out a comparison with the learning vector quantization. Good results were obtained in all experiments, this demonstrating that the approach is very promising.