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A threat monitoring system for intelligent data analytics of network traffic

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Abstract

Security attacks have been increasingly common and cause great harm to people and organizations. Late detection of such attacks increases the possibility of irreparable damage, with high financial losses being a common occurrence. This article proposes TeMIA-NT (ThrEat Monitoring and Intelligent data Analytics of Network Traffic), a real-time flow analysis system that uses parallel flow processing. The main contributions of the TeMIA-NT are (i) the proposal of an architecture for real-time detection of network intrusions that supports high traffic rates, (ii) the use of the structured streaming library, and (iii) two modes of operation: offline and online. The offline operation mode allows evaluating the performance of multiple machine learning algorithms over a given dataset, including metrics such as accuracy and F1-score. The proposed system uses dataframes and the structured streaming engine in online mode, which allows detection of threats in real-time and a quick reaction to attacks. To prevent or minimize the damage caused by security attacks, TeMIA-NT achieves flow-processing rates that reach 50 GB/s.

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Notes

  2. https://github.com/elastic/kibana, accessed in April 2021.

  3. The code, documentation, and license are available at:https://www.gta.ufrj.br/TeMIA-NT/.

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Funding

This work was financed by CNPq, CAPES, FAPERJ, and FAPESP (2018/23292-0, 15/24485-9, 14/50937-1).

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Authors and Affiliations

  1. Grupo de Teleinformática e Automação, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

    Lucas C. B. Guimarães, Gabriel Antonio F. Rebello, Gustavo F. Camilo, Lucas Airam C. de Souza & Otto Carlos M. B. Duarte

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  1. Lucas C. B. Guimarães

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  2. Gabriel Antonio F. Rebello

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  3. Gustavo F. Camilo

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  4. Lucas Airam C. de Souza

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  5. Otto Carlos M. B. Duarte

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Correspondence toLucas C. B. Guimarães.

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Guimarães, L.C.B., Rebello, G.A.F., Camilo, G.F.et al. A threat monitoring system for intelligent data analytics of network traffic.Ann. Telecommun.77, 539–554 (2022). https://doi.org/10.1007/s12243-021-00893-5

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