利用Androguard工具和XGBoost算法检测Android应用中的恶意软件

Uday Sai Kumar, Ashok Yadav, Vrijendra Singh
{"title":"利用Androguard工具和XGBoost算法检测Android应用中的恶意软件","authors":"Uday Sai Kumar, Ashok Yadav, Vrijendra Singh","doi":"10.1109/UPCON56432.2022.9986470","DOIUrl":null,"url":null,"abstract":"Android is the most popular operating system for smartphones and tablets. With its popularity, Android mal ware has also grown dramatically. Many conventional malware detection techniques are now not sufficient, due to sophisticated detection avoidance strategies. According to ongoing research, one harmful Android software is released every 10 seconds. To counter these significant mal ware campaigns, scalable detection approaches require that can provide quick and accurate identification of mal ware apps. To overcome the above issues, we proposed a method to detect malware in Android applications by extracting features like activities, services, requested permissions, and intent filters from the manifest file. Furthermore, the androguard tool is used to disassemble the code and extract all suspicious API calls by reading the dex code. These extracted features are serialized in feather data format for efficient retrieval. After that, the XGBoost algorithm is used to detect the malware. The result of the proposed method gives 97% accuracy.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Detecting Malware in Android Applications by Using Androguard Tool and XGBoost Algorithm\",\"authors\":\"Uday Sai Kumar, Ashok Yadav, Vrijendra Singh\",\"doi\":\"10.1109/UPCON56432.2022.9986470\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Android is the most popular operating system for smartphones and tablets. With its popularity, Android mal ware has also grown dramatically. Many conventional malware detection techniques are now not sufficient, due to sophisticated detection avoidance strategies. According to ongoing research, one harmful Android software is released every 10 seconds. To counter these significant mal ware campaigns, scalable detection approaches require that can provide quick and accurate identification of mal ware apps. To overcome the above issues, we proposed a method to detect malware in Android applications by extracting features like activities, services, requested permissions, and intent filters from the manifest file. Furthermore, the androguard tool is used to disassemble the code and extract all suspicious API calls by reading the dex code. These extracted features are serialized in feather data format for efficient retrieval. After that, the XGBoost algorithm is used to detect the malware. The result of the proposed method gives 97% accuracy.\",\"PeriodicalId\":185782,\"journal\":{\"name\":\"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UPCON56432.2022.9986470\",\"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 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UPCON56432.2022.9986470","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

安卓是智能手机和平板电脑上最流行的操作系统。随着它的普及,Android软件也有了惊人的增长。由于复杂的检测规避策略,许多传统的恶意软件检测技术现在是不够的。根据正在进行的研究,每10秒就有一个有害的Android软件发布。为了应对这些重大的恶意软件活动,可扩展的检测方法需要能够快速准确地识别恶意软件应用程序。为了克服上述问题,我们提出了一种检测Android应用程序中的恶意软件的方法,通过从manifest文件中提取活动、服务、请求权限和意图过滤器等特征。此外,androguard工具用于反汇编代码,并通过读取索引代码提取所有可疑的API调用。这些提取的特征被序列化成羽毛数据格式,便于检索。然后使用XGBoost算法检测恶意软件。结果表明,该方法的准确率为97%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting Malware in Android Applications by Using Androguard Tool and XGBoost Algorithm
Android is the most popular operating system for smartphones and tablets. With its popularity, Android mal ware has also grown dramatically. Many conventional malware detection techniques are now not sufficient, due to sophisticated detection avoidance strategies. According to ongoing research, one harmful Android software is released every 10 seconds. To counter these significant mal ware campaigns, scalable detection approaches require that can provide quick and accurate identification of mal ware apps. To overcome the above issues, we proposed a method to detect malware in Android applications by extracting features like activities, services, requested permissions, and intent filters from the manifest file. Furthermore, the androguard tool is used to disassemble the code and extract all suspicious API calls by reading the dex code. These extracted features are serialized in feather data format for efficient retrieval. After that, the XGBoost algorithm is used to detect the malware. The result of the proposed method gives 97% accuracy.
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