Applying deep learning techniques for Android malware detection

被引：25

作者：

Zegzhda, Peter ^{[1
]}

Zegzhda, Dmitry ^{[1
]}

Pavlenko, Evgeny ^{[1
]}

Ignatev, Gleb ^{[1
]}

机构：

[1] Peter Great St Petersburg Polytech Univ, 29 Politekhnicheskaya Ul, St Petersburg, Russia

来源：

11TH INTERNATIONAL CONFERENCE ON SECURITY OF INFORMATION AND NETWORKS (SIN 2018) | 2018年

关键词：

Information security; Android OS; mobile security; malware; application analysis; deep learning; convolutional neural network; Android application; Android security; malware detection;

D O I：

10.1145/3264437.3264476

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article explores the use of deep learning for malware identification in the Android operating system. Similar studies are considered and, based on their drawbacks, a self-designed approach is proposed for representing an Android application for a convolutional neural network, which consists in constructing an RGB image, the pixels of which are formed from a sequence of pairs of API calls and protection levels. The results of the experimental evaluation of the proposed approach, which are presented in this paper, demonstrate its high efficiency for solving the problem of identifying malicious Android applications.

引用

页数：8

共 14 条

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