{"id":166591,"date":"2014-08-20T00:00:00","date_gmt":"2014-08-20T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/telepathwords-preventing-weak-passwords-by-reading-users-minds\/"},"modified":"2018-10-16T20:27:05","modified_gmt":"2018-10-17T03:27:05","slug":"telepathwords-preventing-weak-passwords-by-reading-users-minds","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/telepathwords-preventing-weak-passwords-by-reading-users-minds\/","title":{"rendered":"Telepathwords: Preventing Weak Passwords by Reading Users’ Minds"},"content":{"rendered":"

To discourage the creation of predictable passwords, vulnerable to guessing attacks, we present Telepathwords. As a user creates a password, Telepathwords makes realtime predictions for the next character that user will type. While the concept is simple, making accurate predictions requires efficient algorithms to model users\u2019 behavior and to employ already-typed characters to predict subsequent ones. We first made the Telepathwords technology available to the public in late 2013 and have since served hundreds of thousands of user sessions.<\/p>\n

We ran a human-subjects experiment to compare password policies that use Telepathwords to those that rely on composition rules, comparing participants\u2019 passwords using two different password-evaluation algorithms. We found that participants create far fewer weak passwords using the Telepathwords-based policies than policies based only on character composition. Participants using Telepathwords were also more likely to report that the password feedback was helpful.<\/p>\n","protected":false},"excerpt":{"rendered":"

To discourage the creation of predictable passwords, vulnerable to guessing attacks, we present Telepathwords. As a user creates a password, Telepathwords makes realtime predictions for the next character that user will type. While the concept is simple, making accurate predictions requires efficient algorithms to model users\u2019 behavior and to employ already-typed characters to predict subsequent […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13554,13558],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-166591","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-computer-interaction","msr-research-area-security-privacy-cryptography","msr-locale-en_us"],"msr_publishername":"USENIX","msr_edition":"Proceedings of the 23rd USENIX Security 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