C. Qi, Shuai Shao, Yanhui Guo, Junhao Peng, Guosheng Xu
{"title":"一种分析Android系统小部件意图的有效方法","authors":"C. Qi, Shuai Shao, Yanhui Guo, Junhao Peng, Guosheng Xu","doi":"10.1145/3456415.3456428","DOIUrl":null,"url":null,"abstract":"With the improvement of the status of mobile phones and other mobile terminals in life, the privacy protection of mobile phones is still a big problem. Aiming at the Android system, the mobile terminal operating system with the largest share today, this paper proposes a widget intention analysis method to solve the problem of how to judge the user's operation intention in privacy protection. This intention analysis method uses MobilenetV3 to extract image information, BiLSTM to extract text information, then constructs joint features together, and uses deep learning technology to realize intention analysis of widgets. Through comparative experiments, our method improves the detection accuracy and reduces the training time for the existing methods.","PeriodicalId":422117,"journal":{"name":"Proceedings of the 2021 9th International Conference on Communications and Broadband Networking","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Efficient Method for Analyzing Widget Intent of Android System\",\"authors\":\"C. Qi, Shuai Shao, Yanhui Guo, Junhao Peng, Guosheng Xu\",\"doi\":\"10.1145/3456415.3456428\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the improvement of the status of mobile phones and other mobile terminals in life, the privacy protection of mobile phones is still a big problem. Aiming at the Android system, the mobile terminal operating system with the largest share today, this paper proposes a widget intention analysis method to solve the problem of how to judge the user's operation intention in privacy protection. This intention analysis method uses MobilenetV3 to extract image information, BiLSTM to extract text information, then constructs joint features together, and uses deep learning technology to realize intention analysis of widgets. Through comparative experiments, our method improves the detection accuracy and reduces the training time for the existing methods.\",\"PeriodicalId\":422117,\"journal\":{\"name\":\"Proceedings of the 2021 9th International Conference on Communications and Broadband Networking\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2021 9th International Conference on Communications and Broadband Networking\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3456415.3456428\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 9th International Conference on Communications and Broadband Networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3456415.3456428","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Efficient Method for Analyzing Widget Intent of Android System
With the improvement of the status of mobile phones and other mobile terminals in life, the privacy protection of mobile phones is still a big problem. Aiming at the Android system, the mobile terminal operating system with the largest share today, this paper proposes a widget intention analysis method to solve the problem of how to judge the user's operation intention in privacy protection. This intention analysis method uses MobilenetV3 to extract image information, BiLSTM to extract text information, then constructs joint features together, and uses deep learning technology to realize intention analysis of widgets. Through comparative experiments, our method improves the detection accuracy and reduces the training time for the existing methods.