Feature Subset for Improving Accuracy of Keystroke Dynamics on Mobile Environment
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초록
Keystroke dynamics user authentication is a behavior-based authentication method which analyzes patternsin how a user enters passwords and PINs to authenticate the user. Even if a password or PIN is revealed toanother user, it analyzes the input pattern to authenticate the user; hence, it can compensate for thedrawbacks of knowledge-based (what you know) authentication. However, users' input patterns are notalways fixed, and each user's touch method is different. Therefore, there are limitations to extracting the samefeatures for all users to create a user's pattern and perform authentication. In this study, we performexperiments to examine the changes in user authentication performance when using feature vectorscustomized for each user versus using all features. User customized features show a mean improvement ofover 6% in error equal rate, as compared to when all features are used.
Abstract
Keystroke dynamics user authentication is a behavior-based authentication method which analyzes patternsin how a user enters passwords and PINs to authenticate the user. Even if a password or PIN is revealed toanother user, it analyzes the input pattern to authenticate the user; hence, it can compensate for thedrawbacks of knowledge-based (what you know) authentication. However, users' input patterns are notalways fixed, and each user's touch method is different. Therefore, there are limitations to extracting the samefeatures for all users to create a user's pattern and perform authentication. In this study, we performexperiments to examine the changes in user authentication performance when using feature vectorscustomized for each user versus using all features. User customized features show a mean improvement ofover 6% in error equal rate, as compared to when all features are used.
참고문헌 (17)
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