Enhancing Automatic Attack Detection through Spectral Decomposition of Network Flows

Author(s)

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

• Document type: Journal article
• Source: 2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022); v. N/A, p. 6-pg., 2022-01-01.

Abstract

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)

• FAPESP's process: 18/23292-0 - ACCRUE-SFI project: advanced collaborative research infrastructure for secure future internet
• Grantee: Otto Carlos Muniz Bandeira Duarte
• Support Opportunities: Regular Research Grants
• FAPESP's process: 15/24485-9 - Future internet for smart cities
• Grantee: Fabio Kon
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
• Grantee: Nelson Luis Saldanha da Fonseca
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 14/50937-1 - INCT 2014: on the Internet of the Future
• Grantee: Fabio Kon
• Support Opportunities: Research Projects - Thematic Grants