{"title":"The pattern-richness of Graphical passwords","authors":"J. Vorster, R. V. Heerden, B. Irwin","doi":"10.1109/ISSA.2016.7802931","DOIUrl":null,"url":null,"abstract":"Conventional (text-based) passwords have shown patterns such as variations on the username, or known passwords such as “password”, “admin” or “12345”. Patterns may similarly be detected in the use of Graphical passwords (GPs). The most significant such pattern - reported by many researchers - is hotspot clustering. This paper qualitatively analyses more than 200 graphical passwords for patterns other than the classically reported hotspots. The qualitative analysis finds that a significant percentage of passwords fall into a small set of patterns; patterns that can be used to form attack models against GPs. In counter action, these patterns can also be used to educate users so that future password selection is more secure. It is the hope that the outcome from this research will lead to improved behaviour and an enhancement in graphical password security.","PeriodicalId":330340,"journal":{"name":"2016 Information Security for South Africa (ISSA)","volume":"37 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Information Security for South Africa (ISSA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSA.2016.7802931","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Conventional (text-based) passwords have shown patterns such as variations on the username, or known passwords such as “password”, “admin” or “12345”. Patterns may similarly be detected in the use of Graphical passwords (GPs). The most significant such pattern - reported by many researchers - is hotspot clustering. This paper qualitatively analyses more than 200 graphical passwords for patterns other than the classically reported hotspots. The qualitative analysis finds that a significant percentage of passwords fall into a small set of patterns; patterns that can be used to form attack models against GPs. In counter action, these patterns can also be used to educate users so that future password selection is more secure. It is the hope that the outcome from this research will lead to improved behaviour and an enhancement in graphical password security.