Detecção de ataques de negação de serviço em redes de computadores através da transformada wavelet 2D

• Ano de defesa: 2012
• Autor(a) principal: Azevedo, Renato Preigschadt de
• Orientador(a): Nunes, Raul Ceretta
• Banca de defesa: Gaspary, Luciano Paschoal , Machado, Renato
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria
• Programa de Pós-Graduação: Programa de Pós-Graduação em Informática
• Departamento: Ciência da Computação
• País: BR
• Palavras-chave em Português: Detecção de anomalias; Detecção de intrusão; Wavelet; Wavelet 2D; Sistemas distribuídos
• Palavras-chave em Inglês: Anomaly detection; Intrusion detection; Wavelet; 2D wavelet; Distributed systems
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/5395

The analysis of network traffic is a key area for the management of fault-tolerant systems, since anomalies in network traffic can affect the availability and quality of service (QoS). Intrusion detection systems in computer networks are used to analyze network traffic in order to detect attacks and anomalies. The analysis based on anomalies allows attacks detection by analyzing the behavior of the traffic network. This work proposes an intrusion detection tool to quickly and effectively detect anomalies in computer networks generated by denial of service (DoS). The detection algorithm is based on the two-dimensional wavelet transform (2D Wavelet), a derived method of signal analysis. The wavelet transform is a mathematical tool with low computational cost that explores the existing information present in the input samples according to the different levels of the transformation. The proposed algorithm detects anomalies directly based on the wavelet coefficients, considering threshold techniques. This operation does not require the reconstruction of the original signal. Experiments were performed using two databases: a synthetic (DARPA) and another one from data collected at the Federal University of Santa Maria (UFSM), allowing analysis of the intrusion detection tool under different scenarios. The wavelets considered for the tests were all from the orthonormal family of Daubechies: Haar (Db1), Db2, Db4 and Db8 (with 1, 2, 4 and 8 null vanishing moments respectively). For the DARPA database we obtained a detection rate up to 100% using the Daubechies wavelet transform Db4, considering normalized wavelet coefficients. For the database collected at UFSM the detection rate was 95%, again considering Db4 wavelet transform with normalized wavelet coefficients.