A Robust Approach for Android Malware Detection Based on Deep Learning

LI Peng-wei; JIANG Yu-qian; XUE Fei-yang; HUANG Jia-jia; XU Chao

doi:10.3969/j.issn.0372-2112.2020.08.007

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A Robust Approach for Android Malware Detection Based on Deep Learning

更新时间：2025-07-16

- A Robust Approach for Android Malware Detection Based on Deep Learning
- Acta Electronica Sinica Vol. 48, Issue 8, Pages: 1502-1508(2020)
- 作者机构：
  
  南京审计大学信息工程学院,江苏,南京,211815
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61802194, No.61902190);Natural Science Research Program of Universities in Jiangsu Province (No.17KJB520015, No.19KJB520040);supported by Collaborative Innovation Center for Audit Information and Engineering (No.18CICA06)
- DOI：10.3969/j.issn.0372-2112.2020.08.007
  CLC： TP391
- Published Online：25 August 2020，
  
  Published：2020
- 稿件说明：
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LI Peng-wei, JIANG Yu-qian, XUE Fei-yang, et al. A Robust Approach for Android Malware Detection Based on Deep Learning[J]. Acta Electronica Sinica, 2020, 48(8): 1502-1508.
DOI：

LI Peng-wei, JIANG Yu-qian, XUE Fei-yang, et al. A Robust Approach for Android Malware Detection Based on Deep Learning[J]. Acta Electronica Sinica, 2020, 48(8): 1502-1508. DOI： 10.3969/j.issn.0372-2112.2020.08.007.

摘要

针对现有Android恶意代码检测方法容易被绕过的问题，提出了一种强对抗性的Android恶意代码检测方法.首先设计实现了动静态分析相结合的移动应用行为分析方法，该方法能够破除多种反分析技术的干扰，稳定可靠地提取移动应用的权限信息、防护信息和行为信息.然后，从上述信息中提取出能够抵御模拟攻击的能力特征和行为特征，并利用一个基于长短时记忆网络（Long Short-Term Memory，LSTM）的神经网络模型实现恶意代码检测.最后通过实验证明了本文所提出方法的可靠性和先进性.

Abstract

Conventional Android malware detection method can easily be evaded. In this study

we propose a detection method of Android malicious code based on short-term memory network (LSTM)

which makes malware more difficult to evade from detection. In this method

a program analysis framework that combines static and dynamic analysis is proposed at first to get the permission information

protection information and behavior information. Secondly

entrenched features such as ability features and behavior features are extracted from the information that provided by the program analysis framework. With the entrenched features

we design a malware detection method based on LSTM model to distinguish benign applications from the malicious ones. Experimental results demonstrate that our approach is more effective and robust in Android malware detection than the state-of-the-art methods.

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