{"title":"基于行为生物识别的MOOC认证方法，具有抗模仿的鲁棒性","authors":"Markus Krause","doi":"10.1145/2556325.2567881","DOIUrl":null,"url":null,"abstract":"Ensuring authorship in online taken exams is a major challenge for e-learning in general and MOOC's in particular. In this paper, we introduce and evaluate a method to verify student identities using stylometry. We present a carefully composed feature set and use it with a K-Nearest Neighbor algorithm. We demonstrate that our method can effectively authenticate authors and is robust against imitation attacks.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":"116 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"A behavioral biometrics based authentication method for MOOC's that is robust against imitation attempts\",\"authors\":\"Markus Krause\",\"doi\":\"10.1145/2556325.2567881\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ensuring authorship in online taken exams is a major challenge for e-learning in general and MOOC's in particular. In this paper, we introduce and evaluate a method to verify student identities using stylometry. We present a carefully composed feature set and use it with a K-Nearest Neighbor algorithm. We demonstrate that our method can effectively authenticate authors and is robust against imitation attacks.\",\"PeriodicalId\":20830,\"journal\":{\"name\":\"Proceedings of the first ACM conference on Learning @ scale conference\",\"volume\":\"116 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the first ACM conference on Learning @ scale conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2556325.2567881\",\"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 first ACM conference on Learning @ scale conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2556325.2567881","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A behavioral biometrics based authentication method for MOOC's that is robust against imitation attempts
Ensuring authorship in online taken exams is a major challenge for e-learning in general and MOOC's in particular. In this paper, we introduce and evaluate a method to verify student identities using stylometry. We present a carefully composed feature set and use it with a K-Nearest Neighbor algorithm. We demonstrate that our method can effectively authenticate authors and is robust against imitation attacks.
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