Enhancing Automatic Attack Detection through Spectral Decomposition of Network Flows

Autor(es)

de Souza, Lucas Airam C. ; Camilo, Gustavo F. ; Campista, Miguel Elias M. ; Costa, Luis Henrique M. K. ; Duarte, Otto Carlos M. B. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022); v. N/A, p. 6-pg., 2022-01-01.

Resumo

Flow classification employs machine learning techniques to identify attacks on computer networks. This classification relies on quantitative features that synthesize the information of packets from the same flow. Conventional features, however, such as packet size and the number of bytes, generate redundancies and do not capture the temporal correlations between the packets in a flow. Automated network attacks generate periodic patterns observable through spectral decomposition, which facilitates classification. This paper proposes FENED (Feature Extraction by Network spEctrum Decomposition), a method to extract features from network data. We consider the packet-arrived order within the same flow using the fast Fourier transform for binary classification. The proposed feature vector contains the module of the spectral components of the flow. Experimental results show that FENED outperforms conventional proposals because it extracts features that consider intra-flow packet-arrival order. (AU)

• Processo FAPESP: 18/23292-0 - Projeto ACCRUE-SFI: infraestrutura avançada e colaborativa de pesquisa para internet do futuro segura
• Beneficiário: Otto Carlos Muniz Bandeira Duarte
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 15/24485-9 - Internet do futuro aplicada a cidades inteligentes
• Beneficiário: Fabio Kon
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• Processo FAPESP: 15/24494-8 - Comunicação e processamento de big data em nuvens e névoas computacionais
• Beneficiário: Nelson Luis Saldanha da Fonseca
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• Processo FAPESP: 14/50937-1 - INCT 2014: da Internet do Futuro
• Beneficiário: Fabio Kon
• Modalidade de apoio: Auxílio à Pesquisa - Temático