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Network Traffic Analysis for Android Malware Detection

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Hybrid Artificial Intelligent Systems (HAIS 2019)

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Abstract

The possibilities offered by the management of huge quantities of equipment and/or networks is attracting a growing number of developers of malware. In this paper, we propose a working methodology for the detection of malicious traffic, based on the analysis of the flow of packets circulating on the network. This objective is achieved through the parameterization of the characteristics of these packages to be analyzed later with supervised learning techniques focused on traffic labeling, so as to enable a proactive response to the large volume of information handled by current filters.

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Notes

  1. 1.

    https://www.genymotion.com/.

  2. 2.

    https://www.virtualbox.org/.

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Authors and Affiliations

  1. University of Deusto, Avenida de las Universidades 24, 48007, Bilbao, Spain

    José Gaviria de la Puerta, Iker Pastor-López, Borja Sanz & Pablo G. Bringas

Authors
  1. José Gaviria de la Puerta
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  2. Iker Pastor-López
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  3. Borja Sanz
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  4. Pablo G. Bringas
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Corresponding author

Correspondence to José Gaviria de la Puerta .

Editor information

Editors and Affiliations

  1. University of León, León, Spain

    Hilde Pérez García

  2. University of León, León, Spain

    Lidia Sánchez González

  3. University of León, León, Spain

    Manuel Castejón Limas

  4. University of A Coruña, Ferrol, Spain

    Héctor Quintián Pardo

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado Rodríguez

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de la Puerta, J.G., Pastor-López, I., Sanz, B., Bringas, P.G. (2019). Network Traffic Analysis for Android Malware Detection. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_40

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  • DOI: https://doi.org/10.1007/978-3-030-29859-3_40

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