{"title":"Smartphone passcode prediction","authors":"Tao Chen, Michael Farcasin, Eric Chan-Tin","doi":"10.1049/iet-ifs.2017.0606","DOIUrl":null,"url":null,"abstract":"Many people now own smartphones and store all their documents such as pictures and financial statements on their phone. To protect this sensitive information, people generally use a passcode to prevent unauthorised access to their phone. Shoulder-surfing attacks are well known. However, contrary to common belief, they are not easy to carry out. Shoulder-surfing attacks to predict the passcode by humans are shown to not be accurate. The authors thus propose an automated algorithm to accurately predict the passcode entered by a victim on her smartphone by recording the video. Their proposed algorithm is able to predict over 92% of numbers entered in fewer than 75 s with training performed once.","PeriodicalId":13305,"journal":{"name":"IET Inf. Secur.","volume":"6 1","pages":"431-437"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Inf. Secur.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/iet-ifs.2017.0606","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Many people now own smartphones and store all their documents such as pictures and financial statements on their phone. To protect this sensitive information, people generally use a passcode to prevent unauthorised access to their phone. Shoulder-surfing attacks are well known. However, contrary to common belief, they are not easy to carry out. Shoulder-surfing attacks to predict the passcode by humans are shown to not be accurate. The authors thus propose an automated algorithm to accurately predict the passcode entered by a victim on her smartphone by recording the video. Their proposed algorithm is able to predict over 92% of numbers entered in fewer than 75 s with training performed once.