Network Traffic Analysis for Android Malware Detection

  1. José Gaviria de la Puerta 1
  2. Iker Pastor-López 1
  3. Borja Sanz 1
  4. Bringas, Pablo G. 1
  1. 1 Universidad de Deusto
    info

    Universidad de Deusto

    Bilbao, España

    ROR https://ror.org/00ne6sr39

Liburua:
Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings
  1. Hilde Pérez García (coord.)
  2. Lidia Sánchez González (coord.)
  3. Manuel Castejón Limas (coord.)
  4. Héctor Quintián Pardo (coord.)
  5. Emilio Corchado Rodríguez (coord.)

Argitaletxea: Springer Suiza

ISBN: 978-3-030-29859-3,  978-3-030-29858-6

Argitalpen urtea: 2019

Orrialdeak: 468-479

Biltzarra: Hybrid Artificial Intelligent Systems (14. 2019. León)

Mota: Biltzar ekarpena

DIALNET GOOGLE SCHOLAR

Laburpena

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.

Datuen iturria: Dialnet