Método híbrido de detecção de intrusão aplicando inteligência artificial

• Ano de defesa: 2018
• Autor(a) principal: Souza, Cristiano Antonio de
• Orientador(a): Machado, Renato Bobsin
• Banca de defesa: Machado, Renato Bobsin , Silva, Rômulo César , Reginato, Romeu
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual do Oeste do Paraná; Foz do Iguaçu
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia Elétrica e Computação
• Departamento: Centro de Engenharias e Ciências Exatas
• País: Brasil
• Palavras-chave em Português: Redes neurais artificiais; K-Nearest neighbor; Segurança computacional
• Palavras-chave em Inglês: Artificial neural networks; K-Nearest neighbor; Network security
• Área do conhecimento CNPq: SISTEMAS DINÂMICOS E ENERGÉTICOS
• Link de acesso: http://tede.unioeste.br/handle/tede/3534

The last decades have been marked by rapid technological development, which was accelerated by the creation of computer networks, and emphatically by the spread and growth of the Internet. As a consequence of this context, private and confidential data of the most diverse areas began to be treated and stored in distributed environments, making vital the security of this data. Due to this fact, the number and variety of attacks on computer systems increased, mainly due to the exploitation of vulnerabilities. Thence, the area of intrusion detection research has gained notoriety, and hybrid detection methods using Artificial Intelligence techniques have been achieving more satisfactory results than the use of such approaches individually. This work consists of a Hybrid method of intrusion detection combining Artificial Neural Network (ANN) and K-Nearest Neighbors KNN techniques. The evaluation of the proposed Hybrid method and the comparison with ANN and KNN techniques individually were developed according to the steps of the Knowledge Discovery in Databases process. For the realization of the experiments, the NSL-KDD public database was selected and, with the attribute selection task, five sub-bases were derived. The experimental results showed that the Hybrid method had better accuracy in relation to ANN in all configurations, whereas in relation to KNN, it reached equivalent accuracy and showed a significant reduction in processing time. Finally, it should be emphasized that among the hybrid configurations evaluated quantitatively and statistically, the best performances in terms of accuracy and classification time were obtained by the hybrid approaches HIB(P25-N75)-C, HIB(P25-N75)-30 and HIB(P25-N75)-20.