A fast unsupervised preprocessing method for network monitoring

Autor(es)

Lopez, Martin Andreoni ; Mattos, Diogo M. F. ; Duarte, Otto Carlos M. B. ; Pujolle, Guy

• Tipo de documento: Artigo Científico
• Fonte: ANNALS OF TELECOMMUNICATIONS; v. 74, n. 3-4, p. 17-pg., 2019-04-01.

Resumo

Identifying a network misuse takes days or even weeks, and network administrators usually neglect zero-day threats until a large number of malicious users exploit them. Besides, security applications, such as anomaly detection and attack mitigation systems, must apply real-time monitoring to reduce the impacts of security incidents. Thus, information processing time should be as small as possible to enable an effective defense against attacks. In this paper, we present a fast preprocessing method for network traffic classification based on feature correlation and feature normalization. Our proposed method couples a normalization and feature selection algorithms. We evaluate the proposed algorithms against three different datasets for eight different machine learning classification algorithms. Our proposed normalization algorithm reduces the classification error rate when compared with traditional methods. Our feature selection algorithm chooses an optimized subset of features improving accuracy by more than 11% within a 100-fold reduction in processing time when compared to traditional feature selection and feature reduction algorithms. The preprocessing method is performed in batch and streaming data, being able to detect concept-drift. (AU)

• Processo FAPESP: 15/24485-9 - Internet do futuro aplicada a cidades inteligentes
• Beneficiário: Fabio Kon
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 15/24514-9 - Projeto stream: segurança em tempo real com elasticidade, analítica e monitoramento
• Beneficiário: Otto Carlos Muniz Bandeira Duarte
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/50937-1 - INCT 2014: da Internet do Futuro
• Beneficiário: Fabio Kon
• Modalidade de apoio: Auxílio à Pesquisa - Temático