烂苹果坏苹果:剖析Google Play恶意软件

Michael Cao, Khaled Ahmed, J. Rubin
{"title":"烂苹果坏苹果:剖析Google Play恶意软件","authors":"Michael Cao, Khaled Ahmed, J. Rubin","doi":"10.1145/3510003.3510161","DOIUrl":null,"url":null,"abstract":"This paper provides an in-depth analysis of Android malware that bypassed the strictest defenses of the Google Play application store and penetrated the official Android market between January 2016 and July 2021. We systematically identified 1,238 such malicious applications, grouped them into 134 families, and manually analyzed one application from 105 distinct families. During our manual analysis, we identified malicious payloads the applications execute, conditions guarding execution of the payloads, hiding techniques applications employ to evade detection by the user, and other implementation-level properties relevant for automated malware detection. As most applications in our dataset contain multiple payloads, each triggered via its own complex activation logic, we also contribute a graph-based representation showing activation paths for all application payloads in form of a control- and data-flow graph. Furthermore, we discuss the capabilities of existing malware detection tools, put them in context of the properties observed in the analyzed malware, and identify gaps and future research directions. We believe that our detailed analysis of the recent, evasive malware will be of interest to researchers and practitioners and will help further improve malware detection tools.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Rotten Apples Spoil the Bunch: An Anatomy of Google Play Malware\",\"authors\":\"Michael Cao, Khaled Ahmed, J. Rubin\",\"doi\":\"10.1145/3510003.3510161\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper provides an in-depth analysis of Android malware that bypassed the strictest defenses of the Google Play application store and penetrated the official Android market between January 2016 and July 2021. We systematically identified 1,238 such malicious applications, grouped them into 134 families, and manually analyzed one application from 105 distinct families. During our manual analysis, we identified malicious payloads the applications execute, conditions guarding execution of the payloads, hiding techniques applications employ to evade detection by the user, and other implementation-level properties relevant for automated malware detection. As most applications in our dataset contain multiple payloads, each triggered via its own complex activation logic, we also contribute a graph-based representation showing activation paths for all application payloads in form of a control- and data-flow graph. Furthermore, we discuss the capabilities of existing malware detection tools, put them in context of the properties observed in the analyzed malware, and identify gaps and future research directions. We believe that our detailed analysis of the recent, evasive malware will be of interest to researchers and practitioners and will help further improve malware detection tools.\",\"PeriodicalId\":202896,\"journal\":{\"name\":\"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)\",\"volume\":\"69 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3510003.3510161\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3510003.3510161","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

本文深入分析了2016年1月至2021年7月期间绕过Google Play应用商店最严格防御并渗透到Android官方市场的Android恶意软件。我们系统地识别了1238个这样的恶意应用程序,将它们分为134个家族,并手动分析了105个不同家族中的一个应用程序。在我们的手动分析过程中,我们确定了应用程序执行的恶意有效负载、保护有效负载执行的条件、应用程序用来逃避用户检测的隐藏技术,以及与自动恶意软件检测相关的其他实现级属性。由于我们数据集中的大多数应用程序包含多个有效负载,每个有效负载都通过其自己的复杂激活逻辑触发,因此我们还提供了一个基于图的表示,以控制和数据流图的形式显示所有应用程序有效负载的激活路径。此外,我们讨论了现有恶意软件检测工具的功能,将它们放在分析恶意软件中观察到的属性的上下文中,并确定差距和未来的研究方向。我们相信,我们对最近的恶意软件的详细分析将引起研究人员和从业人员的兴趣,并将有助于进一步改进恶意软件检测工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rotten Apples Spoil the Bunch: An Anatomy of Google Play Malware
This paper provides an in-depth analysis of Android malware that bypassed the strictest defenses of the Google Play application store and penetrated the official Android market between January 2016 and July 2021. We systematically identified 1,238 such malicious applications, grouped them into 134 families, and manually analyzed one application from 105 distinct families. During our manual analysis, we identified malicious payloads the applications execute, conditions guarding execution of the payloads, hiding techniques applications employ to evade detection by the user, and other implementation-level properties relevant for automated malware detection. As most applications in our dataset contain multiple payloads, each triggered via its own complex activation logic, we also contribute a graph-based representation showing activation paths for all application payloads in form of a control- and data-flow graph. Furthermore, we discuss the capabilities of existing malware detection tools, put them in context of the properties observed in the analyzed malware, and identify gaps and future research directions. We believe that our detailed analysis of the recent, evasive malware will be of interest to researchers and practitioners and will help further improve malware detection tools.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信