Android malware classification based on permission categories using extreme gradient boosting

Togu Novriansyah Turnip, Amsal Situmorang, A. Lumbantobing, Josua Marpaung, S. Situmeang
{"title":"Android malware classification based on permission categories using extreme gradient boosting","authors":"Togu Novriansyah Turnip, Amsal Situmorang, A. Lumbantobing, Josua Marpaung, S. Situmeang","doi":"10.1145/3427423.3427427","DOIUrl":null,"url":null,"abstract":"Mobile malware has become the centerpiece of most security and privacy threats on the Internet. Especially with the openness of the Android market, many malicious apps are hiding in a large number of applications, which makes malware detection more challenging. In this study, eXtreme Gradient Boosting (XGBoost) is used to establish the Android-based malware detection and classification framework. The framework utilizes APK permission categories extracted from Android applications. The comparison of modeling results demonstrates that the XGBoost is especially suitable for Android malware classification and can achieve 74.40% of F1-score with real-world Android application sets.","PeriodicalId":120194,"journal":{"name":"Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3427423.3427427","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Mobile malware has become the centerpiece of most security and privacy threats on the Internet. Especially with the openness of the Android market, many malicious apps are hiding in a large number of applications, which makes malware detection more challenging. In this study, eXtreme Gradient Boosting (XGBoost) is used to establish the Android-based malware detection and classification framework. The framework utilizes APK permission categories extracted from Android applications. The comparison of modeling results demonstrates that the XGBoost is especially suitable for Android malware classification and can achieve 74.40% of F1-score with real-world Android application sets.
Android恶意软件分类基于权限类别使用极端梯度提升
移动恶意软件已经成为互联网上大多数安全和隐私威胁的核心。特别是随着Android市场的开放,很多恶意应用隐藏在大量的应用中,这使得恶意软件的检测更具挑战性。本研究采用极限梯度增强(eXtreme Gradient Boosting, XGBoost)技术建立基于android的恶意软件检测与分类框架。该框架利用从Android应用程序中提取的APK权限类别。建模结果对比表明,XGBoost特别适用于Android恶意软件分类,在真实Android应用集上可以达到74.40%的f1得分。
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