[1]王 斌,李 峰,杨慧婷,等.Android 应用程序漏洞检测方法和工具新进展[J].计算机技术与发展,2024,34(02):9-16.[doi:10. 3969 / j. issn. 1673-629X. 2024. 02. 002]
　WANG Bin,LI Feng,YANG Hui-ting,et al.Recent Progress on Android Application Vulnerability Detection Methods and Tools[J].,2024,34(02):9-16.[doi:10. 3969 / j. issn. 1673-629X. 2024. 02. 002]
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Android 应用程序漏洞检测方法和工具新进展()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年02期
页码:
9-16
栏目:
综述
出版日期:
2024-02-10

文章信息/Info

Title:
Recent Progress on Android Application Vulnerability Detection Methods and Tools
文章编号:
1673-629X(2024)02-0009-08
作者:
王 斌; 李 峰; 杨慧婷; 樊树铭
国网新疆电力有限公司电力科学研究院,新疆 乌鲁木齐 830011
Author(s):
WANG Bin; LI Feng; YANG Hui-ting; FAN Shu-ming
State Grid Xinjiang Electric Power Co. ,Ltd. Electric Power Research Institute,Urumqi 830011,China
关键词:
数据安全; 移动设备安全; Android 应用程序; 漏洞检测; 机器学习; 深度学习
Keywords:
data security; mobile security; Android application; vulnerability detection; machine learning; deep learning
分类号:
TP309. 1
DOI:
10. 3969 / j. issn. 1673-629X. 2024. 02. 002
摘要:
Android 是移动设备和智能设备的主流操作系统,其安全性受到广泛关注。 然而 Android 应用程序普遍存在漏洞或恶意代码,许多学者对 Andriod 应用程序的漏洞检测方法开展了研究。 由于 Android 系统发展迅速,且近年来机器学习和深度学习方法成功应用于漏洞检测,该文对 2016 年至 2022 年间发表的 Android 应用程序漏洞检测的最新成果进行了总结,阐述了涉及的源代码特征提取方法、基于机器学习 / 深度学习的检测方法、传统检测方法等,并给出了详细对比表。 研究表明,仍缺乏 Android 专用的源代码漏洞数据集和工具等,以便对基于机器学习 / 深度学习的 Android 漏洞检测方法提供更有效的支撑。
Abstract:
Android is the mainstream operating system for mobile devices and intelligent devices, whose security has been widelyconcerned. Unfortunately,vulnerabilities or malicious code are often concealed in Android applications. Many scholars have studied thevulnerability detection methods for Android applications. Due to the rapid development of Android system and?
the successful applicationof machine learning and deep learning methods in vulnerability detection in recent years,we survey the latest achievements of Android application vulnerability detection published from 2016 to 2022, describe the involved source code feature extraction methods, detectionmethods based on machine learning / deep learning,traditional detection methods,and propose detailed comparison lists. The review showsthat source code vulnerability data sets and tools dedicated to Android is still needed, which can provide more effective support forAndroid vulnerability detection methods based on machine learning / deep learning.

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更新日期/Last Update: 2024-02-10