DeepRD:LSTM-based Siamese network for Android repackaged applications detection

Run WANG; Benxiao TANG; Li’na WANG

doi:10.11959/j.issn.1000-436x.2018148

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DeepRD:LSTM-based Siamese network for Android repackaged applications detection

Artificial Intelligence and Network Security | 更新时间：2024-06-05

- DeepRD:LSTM-based Siamese network for Android repackaged applications detection
- Journal on Communications Vol. 39, Issue 8, Pages: 69-82(2018)
- 作者机构：
  
  1. 空天信息安全与可信计算教育部重点实验室，湖北武汉 430072
  2. 武汉大学国家网络安全学院，湖北武汉 430072
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(U1536204);The Central University Basic Business Expenses Special Funding for Scientific Research Project(2042018kf1028);The National High Technology Research and Development Program of China (863 Program)(2015AA016004)
- DOI：10.11959/j.issn.1000-436x.2018148
  CLC： TP309.1
- Online First：2018-08，
  
  Published：25 August 2018
- 稿件说明：
移动端阅览
Run WANG, Benxiao TANG, Li’na WANG. DeepRD:LSTM-based Siamese network for Android repackaged applications detection[J]. Journal on Communications, 2018, 39(8): 69-82.
DOI：

Run WANG, Benxiao TANG, Li’na WANG. DeepRD:LSTM-based Siamese network for Android repackaged applications detection[J]. Journal on Communications, 2018, 39(8): 69-82. DOI： 10.11959/j.issn.1000-436x.2018148.

摘要

目前，Android 平台重打包应用检测方法依赖于专家定义特征，不但耗时耗力，而且其特征容易被攻击者猜测。另外，现有的应用特征表示难以在常见的重打包应用类型检测中取得良好的效果，导致在实际检测中存在漏报率较高的现象。针对以上2个问题，提出了一种基于深度学习的重打包应用检测方法，自动地学习程序的语义特征表示。首先，对应用程序进行控制流与数据流分析形成序列特征表示；然后，根据词向量嵌入模型将序列特征转变为特征向量表示，输入孪生网络长短期记忆（LSTM

long short term memory）网络中进行程序特征自学习；最后，将学习到的程序特征通过相似性度量实现重打包应用的检测。在公开数据集AndroZoo上测试发现，重打包应用检测的精准率达到95.7%，漏报率低于6.2%。

Abstract

The state-of-art techniques in Android repackaging detection relied on experts to define features

however

these techniques were not only labor-intensive and time-consuming

but also the features were easily guessed by attackers.Moreover

the feature representation of applications which defined by experts cannot perform well to the common types of repackaging detection

which caused a high false negative rate in the real detection scenario.A deep learning-based repackaged applications detection approach was proposed to learn the program semantic features automatically for addressing the above two issues.Firstly

control and data flow analysis were taken for applications to form a sequence feature representation.Secondly

the sequence features were transformed into vectors based on word embedding model to train a Siamese LSTM network for automatically program feature learning.Finally

repackaged applications were detected based on the similarity measurement of learned program features.Experimental results show that the proposed approach achieves a precision of 95.7% and false negative rate of 6.2% in an open sourced dataset AndroZoo.

关键词

Keywords

references

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