Network Traffic Analysis for Android Malware Detection

• José Gaviria de la Puerta ^[1] ; Iker Pastor-López ^[1] ; Borja Sanz ^[1] ; Bringas, Pablo G. ^[1]
  1. [1] University of Deusto
• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García , Lidia Sánchez González , Manuel Castejón Limas , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2019, ISBN 978-3-030-29858-6, págs. 468-479
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • 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.