基于svm的Android平台恶意软件检测方法

Wenjia Li, Jigang Ge, Guqian Dai
{"title":"基于svm的Android平台恶意软件检测方法","authors":"Wenjia Li, Jigang Ge, Guqian Dai","doi":"10.1109/CSCloud.2015.50","DOIUrl":null,"url":null,"abstract":"In recent years, Android has become one of the most popular mobile operating systems because of numerous mobile applications (apps) it provides. However, the malicious Android applications (malware) downloaded from third-party markets have significantly threatened users' security and privacy, and most of them remain undetected due to the lack of efficient and accurate malware detection techniques. In this paper, we study a malware detection scheme for Android platform using an SVM-based approach, which integrates both risky permission combinations and vulnerable API calls and use them as features in the SVM algorithm. To validate the performance of the proposed approach, extensive experiments have been conducted, which show that the proposed malware detection scheme is able to identify malicious Android applications effectively and efficiently.","PeriodicalId":278090,"journal":{"name":"2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"71","resultStr":"{\"title\":\"Detecting Malware for Android Platform: An SVM-Based Approach\",\"authors\":\"Wenjia Li, Jigang Ge, Guqian Dai\",\"doi\":\"10.1109/CSCloud.2015.50\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, Android has become one of the most popular mobile operating systems because of numerous mobile applications (apps) it provides. However, the malicious Android applications (malware) downloaded from third-party markets have significantly threatened users' security and privacy, and most of them remain undetected due to the lack of efficient and accurate malware detection techniques. In this paper, we study a malware detection scheme for Android platform using an SVM-based approach, which integrates both risky permission combinations and vulnerable API calls and use them as features in the SVM algorithm. To validate the performance of the proposed approach, extensive experiments have been conducted, which show that the proposed malware detection scheme is able to identify malicious Android applications effectively and efficiently.\",\"PeriodicalId\":278090,\"journal\":{\"name\":\"2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"71\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSCloud.2015.50\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSCloud.2015.50","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 71

摘要

近年来,Android已经成为最流行的移动操作系统之一,因为它提供了大量的移动应用程序(app)。然而,从第三方市场下载的恶意Android应用(恶意软件)严重威胁了用户的安全和隐私,由于缺乏高效、准确的恶意软件检测技术,大多数恶意软件无法被检测到。在本文中,我们研究了一种基于SVM的Android平台恶意软件检测方案,该方案集成了风险权限组合和漏洞API调用,并将其作为SVM算法的特征。为了验证所提方法的性能,进行了大量的实验,结果表明所提恶意软件检测方案能够有效地识别出Android恶意应用程序。
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 for Android Platform: An SVM-Based Approach
In recent years, Android has become one of the most popular mobile operating systems because of numerous mobile applications (apps) it provides. However, the malicious Android applications (malware) downloaded from third-party markets have significantly threatened users' security and privacy, and most of them remain undetected due to the lack of efficient and accurate malware detection techniques. In this paper, we study a malware detection scheme for Android platform using an SVM-based approach, which integrates both risky permission combinations and vulnerable API calls and use them as features in the SVM algorithm. To validate the performance of the proposed approach, extensive experiments have been conducted, which show that the proposed malware detection scheme is able to identify malicious Android applications effectively and efficiently.
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:604180095
Book学术官方微信