基于敏感组件函数调用图的安卓重打包恶意软件检测方法

杜瑞颖; 陈晶; 吴聪; 闫晰渝

doi:10.12263/DZXB.20250075

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基于敏感组件函数调用图的安卓重打包恶意软件检测方法

学术论文 | 更新时间：2025-12-10

- 基于敏感组件函数调用图的安卓重打包恶意软件检测方法
- A Detection Method for Android Repackaged Malware Based on Sensitive Component Function Call Graph
- 电子学报 2025年53卷第7期页码：2372-2388
- 作者机构：
  
  1.河南师范大学计算机与信息工程学院，河南新乡 453007
  2.武汉大学国家网络安全学院，湖北武汉 430072
  3.武汉大学空天信息安全与可信计算教育部重点实验室，湖北武汉 430072
  4.武汉大学日照信息技术研究院，山东日照 276800
  5.香港大学工学院，电机电子工程学系，中国香港 999077
- 作者简介：
  
  [ "杜瑞颖女，1964年10月出生于河南省新乡市.现为武汉大学国家网络安全学院教授、博士生导师.主要研究方向为网络空间安全、大模型安全、系统安全、云安全. E-mail: duraying@126.com" ]
  [ "陈晶男，1981年3月出生于湖北省武汉市.现为武汉大学国家网络安全学院副院长、教授、博士生导师.主要研究方向为系统安全、移动安全、云安全. E-mail: chenjing@whu.edu.cn" ]
  [ "吴聪男，1995年10月出生于江西省丰城市.现为香港大学博士后研究员.主要研究方向为分布式智能系统安全. E-mail: congwu@hku.hk" ]
  [ "闫晰渝女，2000年12月出生于河南省商丘市.现为武汉大学国家网络安全学院硕士研究生.主要研究方向为web3链上异常交易风险检测.E-mail: xiyuyan@whu.edu.cn" ]
- 基金信息：
  
  国家重点研发计划(2021YFB2700200;2022YFB3103300);国家自然科学基金(62206203;62076187);湖北省重点研发计划(2022BAA039;2021BAA190);山东省重点研发计划(2022CXPT055)
- DOI：10.12263/DZXB.20250075
  中图分类号： TP309.5;
- 收稿：2025-01-22，
  
  修回：2025-05-09，
  
  纸质出版：2025-07-25
- 稿件说明：
移动端阅览
杜瑞颖, 陈晶, 吴聪, 等. 基于敏感组件函数调用图的安卓重打包恶意软件检测方法[J]. 电子学报, 2025, 53(07): 2372-2388.

DU Rui-ying, CHEN Jing, WU Cong, et al. A Detection Method for Android Repackaged Malware Based on Sensitive Component Function Call Graph[J]. Acta Electronica Sinica, 2025, 53(07): 2372-2388.
杜瑞颖, 陈晶, 吴聪, 等. 基于敏感组件函数调用图的安卓重打包恶意软件检测方法[J]. 电子学报, 2025, 53(07): 2372-2388. DOI：10.12263/DZXB.20250075

DU Rui-ying, CHEN Jing, WU Cong, et al. A Detection Method for Android Repackaged Malware Based on Sensitive Component Function Call Graph[J]. Acta Electronica Sinica, 2025, 53(07): 2372-2388. DOI：10.12263/DZXB.20250075

摘要

安卓系统占据移动操作系统七成以上的市场份额，成为许多不法分子传播恶意软件的平台.其中，重打包恶意软件通过嵌入少量恶意代码到良性软件中，利用大量良性行为掩盖恶意行为，从而绕过普通恶意软件检测方法.然而，当前学术界对重打包恶意软件的研究相对较少，现有基于函数调用图分区的检测方法存在通用性不足的问题，且在敏感API（Application Programming Interface）中心性特征方面未充分考虑恶意行为的语义特征.本文提出了一种安卓重打包恶意软件检测方法Partdroid，该方法通过分析清单文件和smali代码，提取应用程序的组件信息并生成组件函数调用图，将所有含有敏感API的组件的函数调用图合并，利用污点分析的方法发掘组件间调用关系，形成敏感组件函数调用图来避免函数调用图分区的局限性.同时，该方法通过挖掘敏感API与入口函数、交互函数的关系突出恶意行为的特征，并结合中心性算法综合计算敏感API的重要性，避免直接使用中心性算法提取特征的局限性.实验结果表明，本方法对安卓重打包恶意软件检测的综合性能优于其他同类工具，随机森林分类器的F1值和准确率分别达到91.34%和91.93%，投票算法则为91.63%和92.15%.此外，Partdroid在新恶意软件检测中表现突出，从谷歌应用商店随机选取的2 000个应用中检出3个可疑软件.

Abstract

The Android system occupies over 70% of the market share of mobile operating systems

making it a key platform for malicious actors to distribute malware. Repackaged malware embeds a small amount of malicious code into legitimate software

masking malicious activities with a majority of benign behaviors to evade traditional malware detection methods. However

academic research on repackaged malware remains relatively limited. Existing detection methods based on partitioning function call graphs often lack generalizability and fail to fully capture the semantic features of malicious behavior associated with sensitive API(Application Programming Interface) centrality. To solve these problems

we propose Partdroid

a detection method for Android repackaged malware. The method analyzes manifest files and smali code to extract application component information and generate component function call graphs. It combines graphs of components with sensitive APIs and uses taint analysis to uncover inter-component relationships

forming a sensitive component function call graph to overcome partitioning limitations. Additionally

Partdroid highlights malicious behavior by exploring the relationships between sensitive APIs

entry functions

and interaction functions. It also integrates centrality algorithms to calculate the importance of sensitive APIs comprehensively

addressing the limitations of directly using centrality algorithms for feature extraction. Experimental results demonstrate that Partdroid outperforms other tools in detecting Android repackaged malware

achieving an F1 score of 91.34% and accuracy of 91.93% with a random forest classifier

and 91.63% and 92.15% with a voting algorithm. Moreover

Partdroid performs outstandingly in detecting new malware

identifying 3 suspicious software among 2 000 randomly selected applications from the Google Play Store.

关键词

Keywords

references

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