Ladislav Peška, Patrik Veselý, T. Skopal, Krisztián Búza
{"title":"Person Authentication using Visual Representations of Keyboard Typing Dynamics","authors":"Ladislav Peška, Patrik Veselý, T. Skopal, Krisztián Búza","doi":"10.1109/SNAMS58071.2022.10062739","DOIUrl":null,"url":null,"abstract":"In this paper, we focus on the problem of user's authentication through typing dynamics patterns. We specifically focus on small-sized problems, where it is difficult to fully train corresponding machine (deep) learning algorithms from scratch. Instead, we propose a different approach based on the visualization of the typing patterns and subsequent usage of pre-trained feature extractors from the computer vision domain. We evaluated the approach on a publicly-available dataset and results indicate that this is a viable solution capable to improve over several baselines. Moreover, the proposed visual representation of the data contributes to the explainability of AI.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SNAMS58071.2022.10062739","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we focus on the problem of user's authentication through typing dynamics patterns. We specifically focus on small-sized problems, where it is difficult to fully train corresponding machine (deep) learning algorithms from scratch. Instead, we propose a different approach based on the visualization of the typing patterns and subsequent usage of pre-trained feature extractors from the computer vision domain. We evaluated the approach on a publicly-available dataset and results indicate that this is a viable solution capable to improve over several baselines. Moreover, the proposed visual representation of the data contributes to the explainability of AI.