DroidVisor: Android安全应用推荐系统

Pulkit Rustgi, Carol J. Fung, Bahman Rashidi, Bridget T. McInnes
{"title":"DroidVisor: Android安全应用推荐系统","authors":"Pulkit Rustgi, Carol J. Fung, Bahman Rashidi, Bridget T. McInnes","doi":"10.23919/INM.2017.7987440","DOIUrl":null,"url":null,"abstract":"In current Android systems, the application recommendation function is an important feature that users can use to find a similar application to replace a targeted one. The current recommendation system provided through Google and the Google Play store presumably recommends applications similar to a target application while accounting for the popularity of each application. However, it does not take the security features of each application or users preferences into consideration when doing so. In this paper, we propose DroidVisor, an Android tool that provides users with fine-grained and customizable application recommendations. Compared to the Google store recommendation function, DroidVisor does not only use the similarity to a preselected target application, but also considers other metrics such as popularity, security, and usability. More specifically, DroidVisor provides an interface for users to configure the weight of each metric and a recommendation algorithm that generates a list of recommended applications based on the combined scores. We evaluate our proposed criteria and the quality of recommendation through use case studies. Finally, we present our findings through a discussion of accuracy as well as possible ways to improve our recommendation results.","PeriodicalId":119633,"journal":{"name":"2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"DroidVisor: An Android secure application recommendation system\",\"authors\":\"Pulkit Rustgi, Carol J. Fung, Bahman Rashidi, Bridget T. McInnes\",\"doi\":\"10.23919/INM.2017.7987440\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In current Android systems, the application recommendation function is an important feature that users can use to find a similar application to replace a targeted one. The current recommendation system provided through Google and the Google Play store presumably recommends applications similar to a target application while accounting for the popularity of each application. However, it does not take the security features of each application or users preferences into consideration when doing so. In this paper, we propose DroidVisor, an Android tool that provides users with fine-grained and customizable application recommendations. Compared to the Google store recommendation function, DroidVisor does not only use the similarity to a preselected target application, but also considers other metrics such as popularity, security, and usability. More specifically, DroidVisor provides an interface for users to configure the weight of each metric and a recommendation algorithm that generates a list of recommended applications based on the combined scores. We evaluate our proposed criteria and the quality of recommendation through use case studies. Finally, we present our findings through a discussion of accuracy as well as possible ways to improve our recommendation results.\",\"PeriodicalId\":119633,\"journal\":{\"name\":\"2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/INM.2017.7987440\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/INM.2017.7987440","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

在当前的Android系统中,应用程序推荐功能是一个重要的功能,用户可以使用它来寻找类似的应用程序来替代目标应用程序。目前通过Google和Google Play商店提供的推荐系统可能会推荐与目标应用程序相似的应用程序,同时考虑每个应用程序的受欢迎程度。但是,在这样做时,它不会考虑每个应用程序的安全特性或用户首选项。在本文中,我们提出了DroidVisor,一个Android工具,为用户提供细粒度和可定制的应用程序推荐。与Google商店推荐功能相比,DroidVisor不仅使用与预先选择的目标应用程序的相似性,还考虑了流行度、安全性和可用性等其他指标。更具体地说,DroidVisor为用户提供了一个界面来配置每个指标的权重,并提供了一个推荐算法,该算法根据综合得分生成推荐应用程序列表。我们通过用例研究来评估我们提出的标准和推荐的质量。最后,我们通过讨论准确性以及改进推荐结果的可能方法来展示我们的发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DroidVisor: An Android secure application recommendation system
In current Android systems, the application recommendation function is an important feature that users can use to find a similar application to replace a targeted one. The current recommendation system provided through Google and the Google Play store presumably recommends applications similar to a target application while accounting for the popularity of each application. However, it does not take the security features of each application or users preferences into consideration when doing so. In this paper, we propose DroidVisor, an Android tool that provides users with fine-grained and customizable application recommendations. Compared to the Google store recommendation function, DroidVisor does not only use the similarity to a preselected target application, but also considers other metrics such as popularity, security, and usability. More specifically, DroidVisor provides an interface for users to configure the weight of each metric and a recommendation algorithm that generates a list of recommended applications based on the combined scores. We evaluate our proposed criteria and the quality of recommendation through use case studies. Finally, we present our findings through a discussion of accuracy as well as possible ways to improve our recommendation results.
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