DeepRD：基于Siamese LSTM网络的Android重打包应用检测方法

汪润; 唐奔宵; 王丽娜

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

您当前的位置：

首页 >

文章列表页 >

DeepRD：基于Siamese LSTM网络的Android重打包应用检测方法

论文Ⅰ：人工智能与网络安全 | 更新时间：2024-06-05

- DeepRD：基于Siamese LSTM网络的Android重打包应用检测方法
- DeepRD:LSTM-based Siamese network for Android repackaged applications detection
- 通信学报 2018年39卷第8期页码：69-82
- 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：
  
  [ "汪润（1991-），男，安徽安庆人，武汉大学博士生，主要研究方向为 Android 安全与隐私、AI安全等。" ]
  [ "唐奔宵（1991-），男，湖北黄石人，武汉大学博士生，主要研究方向为移动安全与隐私、系统安全等。" ]
  [ "王丽娜（1964-），女，辽宁营口人，博士，武汉大学教授、博士生导师，主要研究方向为网络安全、信息隐藏、AI安全等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(U1536204);中央高校基本科研业务费专项基金资助项目(2042018kf1028);国家高技术研究发展计划（“863”计划）基金资助项目(2015AA016004)
- DOI：10.11959/j.issn.1000-436x.2018148
  中图分类号： TP309.1
- 网络首发：2018-08，
  
  纸质出版：2018-08-25
- 稿件说明：
移动端阅览
汪润, 唐奔宵, 王丽娜. DeepRD：基于Siamese LSTM网络的Android重打包应用检测方法[J]. 通信学报, 2018,39(8):69-82.

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.
汪润, 唐奔宵, 王丽娜. DeepRD：基于Siamese LSTM网络的Android重打包应用检测方法[J]. 通信学报, 2018,39(8):69-82. DOI： 10.11959/j.issn.1000-436x.2018148.

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

ZHOU W , ZHOU Y J , JIANG X X , et al . Detecting repackaged smartphone applications in third-party Android marketplaces [C ] // The Second ACM Conference on Data and Application Security and Privacy . 2012 : 317 - 326 .

卿斯汉 . Android 安全研究进展 [J ] . 软件学报 , 2016 , 27 ( 1 ): 45 - 71 .

QING S H . Research progress on Android security [J ] . Journal of Software , 2016 , 27 ( 1 ): 45 - 71 .

文伟平 , 梅瑞 , 宁戈 , 等 . Android 恶意软件检测技术分析和应用研究 [J ] . 通信学报 , 2014 , 35 ( 8 ): 78 - 86 .

WEN W P , MEI R , NING G , et al . Malware detection technology analysis and applied research of Android platform [J ] . Journal on Communications , 2014 , 35 ( 8 ): 78 - 86 .

张玉清 , 王凯 , 杨欢 , 等 . Android安全综述 [J ] . 计算机研究与发展 , 2014 , 51 ( 7 ): 1385 - 1396 .

ZHANG Y Q , WANG K , YANG H , et al . Survey of Android OS security [J ] . Journal of Computer Research and Development , 2014 , 51 ( 7 ): 1385 - 1396 .

张玉清 , 方喆君 , 王凯 , 等 . Android 安全漏洞挖掘技术综述 [J ] . 计算机研究与发展 , 2015 , 52 ( 10 ): 2167 - 2177 .

ZHANG Y Q , FANG Z J , WANG K , et al . Survey of Android vulnerability detection [J ] . Journal of Computer Research and Development , 2015 , 52 ( 10 ): 2167 - 2177 .

杨威 , 肖旭生 , 李邓锋 , 等 . 移动应用安全解析学:成果与挑战 [J ] . 信息安全学报 , 2016 , 1 ( 2 ): 1 - 14 .

YANG W , XIAO X S , LI D F , et al . Security analytics for mobile apps:achievements and challenges [J ] . Journal of Cyber Security , 2016 , 1 ( 2 ): 1 - 14 .

刘新宇 , 翁健 , 张悦 , 等 . 基于 APK 签名信息反馈的 Android 恶意应用检测 [J ] . 通信学报 , 2017 , 38 ( 5 ): 190 - 198 .

LIU X Y , WENG J , ZHANG Y , et al . Android malware detection based on APK signature information feedback [J ] . Journal on Communications , 2017 , 38 ( 5 ): 190 - 198 .

杨欢 , 张玉清 , 胡予濮 , 等 . 基于多类特征的 Android 应用恶意行为检测系统 [J ] . 计算机学报 , 2014 , 37 ( 1 ): 15 - 27 .

YANG H , ZHANG Y Q , HU Y P , et al . A malware behavior detection system of Android applications based on multi-class features [J ] . Chinese Journal of Computers , 2014 , 37 ( 1 ): 15 - 27 .

SADEGHI A , BAGHERI H , GARCIA J , et al . A taxonomy and qualitative comparison of program analysis techniques for security assessment of Android software [J ] . IEEE Transactions on Software Engineering , 2017 , 43 ( 6 ): 492 - 530 .

TIAN K , YAO D D , RYDER B G , et al . Detection of repackaged Android malware with code-heterogeneity features [J ] . IEEE Transactions on Dependable and Secure Computing , 2017 ,PP( 99 ):1.

FAN M , LIU J , WANG W , et al . DAPASA:detecting Android piggybacked apps through sensitive subgraph analysis [J ] . IEEE Transactions on Information Forensics and Security , 2017 , 12 ( 8 ): 1772 - 1785 .

LI L , LI D,BISSYANDÉ T F , et al . Understanding Android app piggybacking:a systematic study of malicious code grafting [J ] . IEEE Transactions on Information Forensics and Security , 2017 , 12 ( 6 ): 1269 - 1284 .

ZHOU W , ZHOU Y J , GRACE M , et al . Fast,scalable detection of piggybacked mobile applications [C ] // The Third ACM Conference on Data and Application Security and Privacy . 2013 : 185 - 196 .

LI L , LI D,BISSYANDÉ T F , et al . Automatically locating malicious packages in piggybacked Android apps [C ] // The 4th International Conference on Mobile Software Engineering and Systems . 2017 : 170 - 174 .

ANDERSON H S , KHARKAR A , FILAR B , et al . Evading machine learning malware detection [C ] // Black Hat USA . 2017 .

DEMONTIS A , MELIS M , BIGGIO B , et al . Yes,machine learning can be more secure! a case study on Android malware detection [J ] . IEEE Transactions on Dependable and Secure Computing , 2017 ,doi:10.1109/TDSC.2017.2700270.

YANG W , KONG D , XIE T , et al . Malware detection in adversarial settings:exploiting feature evolutions and confusions in Android apps [C ] // The 33rd Annual Computer Security Applications Conference . 2017 : 288 - 302 .

刘剑 , 苏璞睿 , 杨珉 , 等 . 软件与网络安全研究综述 [J ] . 软件学报 , 2018 , 29 ( 1 ): 42 - 68 .

LIU J , SU P R , YANG M , et al . Software and cyber security-a survey [J ] . Journal of Software , 2018 , 29 ( 1 ): 42 - 68 .

LECUN Y , BENGIO Y , HINTON G . Deep learning [J ] . Nature , 2015 , 521 ( 7553 ): 436 - 444 .

HE K , ZHANG X , REN S , et al . Deep residual learning for image recognition [C ] // The IEEE Conference on Computer Vision and Pattern Recognition . 2016 : 770 - 778 .

HINTON G , DENG L , YU D , et al . Deep neural networks for acoustic modeling in speech recognition:the shared views of four research groups [J ] . IEEE Signal Processing Magazine , 2012 , 29 ( 6 ): 82 - 97 .

PENNINGTON J , SOCHER R , MANNING C . Glove:global vectors for word representation [C ] // The 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 2014 : 1532 - 1543 .

LI Z , ZOU D Q , XU S H , et al . VulDeePecker:a deep learning-based system for vulnerability detection [C ] // NDSS . 2018 .

YUAN Z , LU Y , WANG Z , et al . Droid-Sec:deep learning in Android malware detection [J ] . ACM SIGCOMM Computer Communication Review , 2014 , 44 ( 4 ): 371 - 372 .

WANG S , LIU T , TAN L . Automatically learning semantic features for defect prediction [C ] // The 38th International Conference on Software Engineering . 2016 : 297 - 308 .

PRADEL M , SEN K.Deep learning to find bugs . technical report [R ] . TU Darmstadt,Department of Computer Science . 2017 .

SHIN E , SONG D , MOAZZEZI R . Recognizing functions in binaries with neural networks [C ] // The 24th USENIX Conference on Security Symposium . 2015 : 611 - 626 .

CHUA Z L , SHEN S , SAXENA P , et al . Neural nets can learn function type signatures from binaries [C ] // The 26th USENIX Conference on Security Symposium . 2017 : 99 - 116 .

XU X , LIU C , FENG Q , et al . Neural network-based graph embedding for cross-platform binary code similarity detection [C ] // The 2017 ACM SIGSAC Conference on Computer and Communications Security . 2017 : 363 - 376 .

KOLOSNJAJI B , ZARRAS A , WEBSTER G , et al . Deep learning for classification of malware system call sequences [C ] // Australasian Joint Conference on Artificial Intelligence . 2016 : 137 - 149 .

DAVID O E , NETANYAHU N S . Deepsign:deep learning for automatic malware signature generation and classification [C ] // 2015 International Joint Conference on Neural Networks (IJCNN) . 2015 : 1 - 8 .

HOU S , SAAS A , CHEN L , et al . Deep4maldroid:a deep learning framework for Android malware detection based on linux kernel system call graphs [C ] // Web Intelligence Workshops (WIW) . 2016 : 104 - 111 .

ALLIX K , BISSYANDE T F , KLEIN J , et al . AndroZoo:collecting millions of Android apps for the research community [C ] // The 13th Working Conference on Mining Software Repositories . 2016 : 468 - 471 .

CRUSSELL J , GIBLER C , CHEN H . Attack of the clones:detecting cloned applications on Android markets [C ] // European Symposium on Research in Computer Security . 2012 : 37 - 54 .

CRUSSELL J , GIBLER C , CHEN H . Andarwin:scalable detection of semantically similar Android applications [C ] // European Symposium on Research in Computer Security . 2013 : 182 - 199 .

CHEN K , LIU P , ZHANG Y . Achieving accuracy and scalability simultaneously in detecting application clones on Android markets [C ] // The 36th International Conference on Software Engineering . 2014 : 175 - 186 .

CHEN K , WANG P , LEE Y , et al . Finding unknown malice in 10 seconds:mass vetting for new threats at the google-play scale [C ] // The 24th USENIX Conference on Security Symposium . 2015 : 659 - 674 .

WANG H Y , GUO Y , MA Z , et al . Wukong:a scalable and accurate two-phase approach to Android app clone detection [C ] // The 2015 International Symposium on Software Testing and Analysis . 2015 : 71 - 82 .

王浩宇 , 王仲禹 , 郭耀 , 等 . 基于代码克隆检测技术的 Android 应用重打包检测 [J ] . 中国科学:信息科学 , 2014 , 44 : 142 - 157 .

WANG H Y , WANG Z Y , GUO Y , et al . Detecting repackaged Android applications based on code clone detection technique [J ] . Science China Information Sciences , 2014 , 44 ( 1 ): 142 - 157 .

ZHANG F , HUANG H , ZHU S , et al . ViewDroid:towards obfuscation-resilient mobile application repackaging detection [C ] // The 2014 ACM conference on Security and Privacy in Wireless ＆ Mobile Networks . 2014 : 25 - 36 .

SUN M , LI M , LUI J . Droideagle:seamless detection of visuallysimilar Android apps [C ] // The 8th ACM Conference on Security ＆Privacy in Wireless and Mobile Networks . 2015 : 1 - 9 .

SHAO Y , LUO X , QIAN C , et al . Towards a scalable resource-driven approach for detecting repackaged Android applications [C ] // The 30th Annual Computer Security Applications Conference . 2014 : 56 - 65 .

SOH C , TAN H B K , ARNATOVICH Y L , et al . Detecting clones in Android applications through analyzing user interfaces [C ] // The 2015 IEEE 23rd International Conference on Program Comprehension . 2015 : 163 - 173 .

ZHANG M , DUAN Y , YIN H , et al . Semantics-aware Android malware classification using weighted contextual API dependency graphs [C ] // The 2014 ACM SIGSAC Conference on Computer and Communications Security . 2014 : 1105 - 1116 .

AAFER Y , DU W , YIN H . Droidapiminer:mining api-level features for robust malware detection in Android [C ] // International Conference on Security and Privacy in Communication Systems . 2013 : 86 - 103 .

VALLEE-RAI R , HENDREN L J . Jimple:simplifying Java bytecode for analyses and transformations [R ] . Technical Report,Sable Group,McGill University,Montreal,Canada , 1998 .

MUELLER J , THYAGARAJAN A . Siamese recurrent architectures for learning sentence similarity [C ] // AAAI . 2016 : 2786 - 2792 .

BROMLEY J , GUYON I , LECUN Y , et al . Signature verification using a “siamese” time delay neural network [C ] // Advances in Neural Information Processing Systems . 1994 : 737 - 744 .

LI L , BISSYANDÉ T F , KLEIN J , , 等 . An investigation into the use of common libraries in Android apps [C ] // The 23rd International Conference on Software Analysis,Evolution,and Reengineering (SANER) . 2016 : 403 - 414 .

汪润 , 王丽娜 , 唐奔宵 , 等 . SPRD:基于应用 UI 和程序依赖图的Android重打包应用快速检测方法 [J ] . 通信学报 , 2018 , 39 ( 3 ): 159 - 171 .

WANG R , WANG L N , TANG B X , et al . SPRD:fast application repackaging detection approach in Android based on application’s UI and program dependency graph [J ] . Journal on Communications , 2018 , 39 ( 3 ): 159 - 171 .

浏览量

1961

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

SPRD：基于应用UI和程序依赖图的Android重打包应用快速检测方法

医学影像无监督异常检测技术综述

基于成本敏感CNN-BiLSTM网络的目标可见性预测方法

工业雾霾环境下多模态语义辅助的毫米波波束预测方法

基于置信度差异与熵最小化的跨时间鲁棒射频指纹识别方法