Detecting malicious Android applications based on the network packets generated

被引：4

作者：

de la Puerta, Jose Gaviria ^{[1
]}

Pastor-Lopez, Iker ^{[1
]}

Porto, Igone ^{[1
]}

Sanz, Borja ^{[1
]}

Garcia Bringas, Pablo ^{[1
]}

机构：

[1] Univ Deusto, Ave Univ 24, Bilbao 48007, Spain

来源：

NEUROCOMPUTING | 2021年 / 456卷

关键词：

Malware detection; Machine learning; Network traffic analysis;

D O I：

10.1016/j.neucom.2020.08.095

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Widespread communication by mobile devices today has promoted the use of huge amounts of information on them. Malware applications are undergoing exponential growth, directly attacking these smart phones to steal information. For this reason, we have created a methodology to analyze the network packets sent by any type of mobile applications in order to validate their behavior. In order to solve this problem, we have used supervised learning systems in an attempt to modelize the traffic communication with a set of previously labeled data. This will help to actively detect if applications installed in mobile devices could be tagged as malicious. (c) 2021 Elsevier B.V. All rights reserved.

引用

页码：629 / 636

页数：8

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