{"title":"使用现成的猜测攻击评估密码强度计的准确性","authors":"David Pereira, J. Ferreira, A. Mendes","doi":"10.1109/ISSREW51248.2020.00079","DOIUrl":null,"url":null,"abstract":"In this paper we measure the accuracy of password strength meters (PSMs) using password guessing resistance against off-the-shelf guessing attacks. We consider 13 PSMs, 5 different attack tools, and a random selection of 60,000 passwords extracted from three different datasets of real-world password leaks. Our results show that a significant percentage of passwords classified as strong were cracked, thus suggesting that current password strength estimation methods can be improved.","PeriodicalId":202247,"journal":{"name":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Evaluating the Accuracy of Password Strength Meters using Off-The-Shelf Guessing Attacks\",\"authors\":\"David Pereira, J. Ferreira, A. Mendes\",\"doi\":\"10.1109/ISSREW51248.2020.00079\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we measure the accuracy of password strength meters (PSMs) using password guessing resistance against off-the-shelf guessing attacks. We consider 13 PSMs, 5 different attack tools, and a random selection of 60,000 passwords extracted from three different datasets of real-world password leaks. Our results show that a significant percentage of passwords classified as strong were cracked, thus suggesting that current password strength estimation methods can be improved.\",\"PeriodicalId\":202247,\"journal\":{\"name\":\"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)\",\"volume\":\"196 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSREW51248.2020.00079\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSREW51248.2020.00079","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evaluating the Accuracy of Password Strength Meters using Off-The-Shelf Guessing Attacks
In this paper we measure the accuracy of password strength meters (PSMs) using password guessing resistance against off-the-shelf guessing attacks. We consider 13 PSMs, 5 different attack tools, and a random selection of 60,000 passwords extracted from three different datasets of real-world password leaks. Our results show that a significant percentage of passwords classified as strong were cracked, thus suggesting that current password strength estimation methods can be improved.