Android恶意软件及其家族的特征

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2025-01-06 DOI:10.1145/3708500
Tejpal Sharma, Dhavleesh Rattan
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引用次数: 0

摘要

如今,智能手机使我们的生活更轻松,成为我们必不可少的小工具。除了打电话,手机还有各种各样的用途,比如银行、聊天、数据存储、上网和运行让生活更轻松的应用程序。因此,攻击者正在开发新的方法或恶意软件来窃取智能手机数据。首先,该研究概述了各种类型的Android恶意软件家族,Android恶意软件的演变及其对检测技术的影响。我们报告恶意软件时间表和Android应用程序数据集及其源web链接。数据是从最近的各种研究和报告中收集的。在这项研究中,我们报告了384个Android恶意软件家族及其发现年份,即从2001年到2020年。根据他们在设备上造成的故障,我们将这些家庭分为11类。关于数据集的信息分为三类,以及它们的源链接。恶意软件的分类和时间表将使研究人员更容易根据恶意软件的类别和他们所执行的活动来关注未来的趋势。各种开放的问题和未来的挑战也为未来的研究人员解决。
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Characterization of Android Malwares and their families
Nowadays, smartphones have made our lives easier and have become essential gadgets for us. Apart from calling, mobiles are used for various purposes, such as banking, chatting, data storage, connecting to the internet and running apps which make life easier. Therefore, attackers are developing new methods or malware to steal smartphone data. Primarily, the study outlines various types of Android malware families, the evolution of Android malware and its effects on detection techniques over time. We report malware timelines and Android app datasets with their source web links. Data is collected from various recent studies and reported. In this study, we have reported 384 Android malware families and their year of discovery, i.e., from 2001 to 2020. According to the malfunctions they perform on the device, we categorized the families into 11 types. Information about datasets which is divided into three categories, along with their source links is presented. The categorization and timeline of malware will make it easy for researchers to focus on upcoming trends according to the malware category and activities they perform. Various open issues and future challenges are also addressed for future researchers.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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