Android Malware Detection Amid COVID-19

Rohit Srivastava, R. Mishra, Vivek Kumar, H. Shukla, Neha Goyal, Chandrabhan Singh
{"title":"Android Malware Detection Amid COVID-19","authors":"Rohit Srivastava, R. Mishra, Vivek Kumar, H. Shukla, Neha Goyal, Chandrabhan Singh","doi":"10.1109/SMART50582.2020.9337105","DOIUrl":null,"url":null,"abstract":"Mobile is a rapidly growing web environment that attracts malware developers around the world. Smart phones, especially android phones are widely used and are the most popular new target for malware attacks. Most common type of malware found to attack android users was an unauthorized app repackaged as a normal app through a third party, unofficial app store. New apps found in the app store are hard to identify as malicious. Our work develops a malware detector and analyzer. This paper also links insights about malware attacks in COVID-19 on mobile devices. To meet the objectives, a model is implemented that extracts the inherent features of android application file and analyzes them for quick and accurate analysis. The model classifies the apps more accurately as benign or malicious.","PeriodicalId":129946,"journal":{"name":"2020 9th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 9th International Conference System Modeling and Advancement in Research Trends (SMART)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMART50582.2020.9337105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Mobile is a rapidly growing web environment that attracts malware developers around the world. Smart phones, especially android phones are widely used and are the most popular new target for malware attacks. Most common type of malware found to attack android users was an unauthorized app repackaged as a normal app through a third party, unofficial app store. New apps found in the app store are hard to identify as malicious. Our work develops a malware detector and analyzer. This paper also links insights about malware attacks in COVID-19 on mobile devices. To meet the objectives, a model is implemented that extracts the inherent features of android application file and analyzes them for quick and accurate analysis. The model classifies the apps more accurately as benign or malicious.
COVID-19背景下的Android恶意软件检测
手机是一个快速发展的网络环境,吸引了世界各地的恶意软件开发者。智能手机,尤其是安卓手机被广泛使用,是最受恶意软件攻击的新目标。攻击安卓用户的最常见恶意软件类型是通过第三方非官方应用商店将未经授权的应用重新包装为正常应用。在应用商店中发现的新应用很难识别为恶意应用。我们的工作是开发一个恶意软件检测器和分析器。本文还链接了关于COVID-19在移动设备上的恶意软件攻击的见解。为了实现这一目标,实现了一个模型,提取android应用程序文件的固有特征并对其进行分析,从而实现快速准确的分析。该模型更准确地将应用程序分类为良性或恶意。
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 求助全文
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