{"title":"使用机器学习技术的移动设备基于行为的安全","authors":"S. Rashad, Jonathan M. R. Byrd","doi":"10.5121/IJAIA.2018.9401","DOIUrl":null,"url":null,"abstract":"The goal of this research project is to design and implement a mobile application and machine learning techniques to solve problems related to the security of mobile devices. We introduce in this paper a behavior-based approach that can be applied in a mobile environment to capture and learn the behavior of mobile users. The proposed system was tested using Android OS and the initial experimental results show that the proposed technique is promising, and it can be used effectively to solve the problem of anomaly detection in mobile devices.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Behavior-Based Security for Mobile Devices Using Machine Learning Techniques\",\"authors\":\"S. Rashad, Jonathan M. R. Byrd\",\"doi\":\"10.5121/IJAIA.2018.9401\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The goal of this research project is to design and implement a mobile application and machine learning techniques to solve problems related to the security of mobile devices. We introduce in this paper a behavior-based approach that can be applied in a mobile environment to capture and learn the behavior of mobile users. The proposed system was tested using Android OS and the initial experimental results show that the proposed technique is promising, and it can be used effectively to solve the problem of anomaly detection in mobile devices.\",\"PeriodicalId\":93188,\"journal\":{\"name\":\"International journal of artificial intelligence & applications\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of artificial intelligence & applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/IJAIA.2018.9401\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of artificial intelligence & applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/IJAIA.2018.9401","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Behavior-Based Security for Mobile Devices Using Machine Learning Techniques
The goal of this research project is to design and implement a mobile application and machine learning techniques to solve problems related to the security of mobile devices. We introduce in this paper a behavior-based approach that can be applied in a mobile environment to capture and learn the behavior of mobile users. The proposed system was tested using Android OS and the initial experimental results show that the proposed technique is promising, and it can be used effectively to solve the problem of anomaly detection in mobile devices.