A Large-Scale Study of Android Malware Development Phenomenon on Public Malware Submission and Scanning Platform

IF 5.7 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Big Data Pub Date : 2018-01-08 DOI:10.1109/TBDATA.2018.2790439
Heqing Huang;Cong Zheng;Junyuan Zeng;Wu Zhou;Sencun Zhu;Peng Liu;Ian Molloy;Suresh Chari;Ce Zhang;Quanlong Guan
{"title":"A Large-Scale Study of Android Malware Development Phenomenon on Public Malware Submission and Scanning Platform","authors":"Heqing Huang;Cong Zheng;Junyuan Zeng;Wu Zhou;Sencun Zhu;Peng Liu;Ian Molloy;Suresh Chari;Ce Zhang;Quanlong Guan","doi":"10.1109/TBDATA.2018.2790439","DOIUrl":null,"url":null,"abstract":"With the steady growth of Android malware, we suspect that, during the malware development phase, some Android malware writers use the popular public scanning services (e.g., VirusTotal) for testing the evasion capability of their malware samples, which we name Android malware development cases (AMDs). In this work, we design an AMD hunter in the context of VirusTotal to hunt for AMDs and reveal new threats for Android. First, the AMD hunter sifts through millions of file submissions on VirusTotal efficiently and alert more suspicious submission traces. Second, it performs package level analysis, static code and dynamic analyses on the APKs of the suspicious submissions to validate the AMDs. The implemented hunter has been used in a leading security company for 4 months, which processed 153 million of submissions on VirusTotal, and identified 1,623 AMDs with 13,855 samples from 83 countries. We also performed case studies on 890 malware samples selected from the identified AMDs, which revealed lots of new threats, including the development cases of fake system/banking phishing app, new rooting exploits, new JavaScript based threats, new evasions and AV probing malware. We wrote industry research articles about some AMDs and notified other security vendors to help patch their false negatives. Besides raising the awareness of the existence of AMDs, more importantly, our research provides the first systematic and efficient way to study the malware development phenomenon on VirusTotal. We will share all the samples of the identified AMDs with the research community.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"7 2","pages":"255-270"},"PeriodicalIF":5.7000,"publicationDate":"2018-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBDATA.2018.2790439","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Big Data","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/8248752/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 4

Abstract

With the steady growth of Android malware, we suspect that, during the malware development phase, some Android malware writers use the popular public scanning services (e.g., VirusTotal) for testing the evasion capability of their malware samples, which we name Android malware development cases (AMDs). In this work, we design an AMD hunter in the context of VirusTotal to hunt for AMDs and reveal new threats for Android. First, the AMD hunter sifts through millions of file submissions on VirusTotal efficiently and alert more suspicious submission traces. Second, it performs package level analysis, static code and dynamic analyses on the APKs of the suspicious submissions to validate the AMDs. The implemented hunter has been used in a leading security company for 4 months, which processed 153 million of submissions on VirusTotal, and identified 1,623 AMDs with 13,855 samples from 83 countries. We also performed case studies on 890 malware samples selected from the identified AMDs, which revealed lots of new threats, including the development cases of fake system/banking phishing app, new rooting exploits, new JavaScript based threats, new evasions and AV probing malware. We wrote industry research articles about some AMDs and notified other security vendors to help patch their false negatives. Besides raising the awareness of the existence of AMDs, more importantly, our research provides the first systematic and efficient way to study the malware development phenomenon on VirusTotal. We will share all the samples of the identified AMDs with the research community.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于公共恶意软件提交与扫描平台的Android恶意软件开发现象的大规模研究
随着Android恶意软件的稳步增长,我们怀疑,在恶意软件开发阶段,一些Android恶意软件编写者使用流行的公共扫描服务(例如,VirusTotal)来测试其恶意软件样本的逃避能力,我们将其命名为Android恶意软件开发案例(amd)。在这项工作中,我们在VirusTotal的背景下设计了一个AMD猎人来寻找AMD并揭示Android的新威胁。首先,AMD的“猎人”会在VirusTotal上高效地筛选数百万份提交的文件,并提醒更多可疑的提交痕迹。其次,对可疑提交的apk进行包级分析、静态代码和动态分析,验证amd。实施的猎人已经在一家领先的安全公司中使用了4个月,该公司在VirusTotal上处理了1.53亿份提交,并从83个国家的13855个样本中识别出1623个amd。我们还对从已识别的amd中选择的890个恶意软件样本进行了案例研究,发现了许多新的威胁,包括假系统/银行网络钓鱼应用的开发案例,新的扎根漏洞,新的基于JavaScript的威胁,新的逃避和AV探测恶意软件。我们撰写了关于一些amd的行业研究文章,并通知其他安全供应商帮助修补它们的误报。除了提高对amd存在的认识外,更重要的是,我们的研究提供了第一个系统和有效的方法来研究VirusTotal上的恶意软件开发现象。我们将与研究界分享所有已确定的amd样本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
期刊最新文献
2025 Reviewers List* Temporal Recommendation Based on Adaptive Deep Matrix Factorization Differential Encoding for Improved Representation Learning Over Graphs Two-Step Nyström Sampling for Large-Scale Kernel Approximation Bridging User Dynamic Preferences: A Unified Bridge-Based Diffusion Model for Next POI Recommendation
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1