SISTEMA DE DETECÇÃO DE INTRUSOS EM ATAQUES ORIUNDOS DE BOTNETS UTILIZANDO MÉTODO DE DETECÇÃO HÍBRIDO

• Ano de defesa: 2011
• Autor(a) principal: CUNHA NETO, Raimundo Pereira da
• Orientador(a): ABDELOUAHAB, Zair
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: Engenharia
• País: BR
• Palavras-chave em Português: Segurança de Redes; Botnets; Sistema de Detecção de Intrusos - IDS; Otimização por Enxame da Partícula - PSO; Redes Neurais Artificiais
• Palavras-chave em Inglês: Network Security; Botnet; Intrusion Detection System - IDS; Particle Swarm Optimization - PSO; Artificial Neural Networks
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/480

The defense mechanisms expansion for cyber-attacks combat led to the malware evolution, which have become more structured to break these new safety barriers. Among the numerous malware, Botnet has become the biggest cyber threat due to its ability of controlling, the potentiality of making distributed attacks and because of the existing structure of control. The intrusion detection and prevention has had an increasingly important role in network computer security. In an intrusion detection system, information about the current situation and knowledge about the attacks contribute to the effectiveness of security process against this new cyber threat. The proposed solution presents an Intrusion Detection System (IDS) model which aims to expand Botnet detectors through active objects system by proposing a technology with collect by sensors, preprocessing filter and detection based on signature and anomaly, supported by the artificial intelligence method Particle Swarm Optimization (PSO) and Artificial Neural Networks.