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Hidden markov model (“HMM”)-based user authentication using keystroke dynamics
Hidden markov model (“HMM”)-based user authentication using keystroke dynamics
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机译:使用击键动力学的基于隐马尔可夫模型(“ HMM”)的用户身份验证
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摘要
Hidden Markov Models (“HMMs”) are used to analyze keystroke dynamics measurements collected as a user types a predetermined string on a keyboard. A user enrolls by typing the predetermined string several times; the enrollment samples are used to train a HMM to identify the user. A candidate who claims to be the user provides a typing sample, and the HMM produces a probability to estimate the likelihood that the candidate is the user he claims to be. A computationally-efficient method for preparing HMMs to analyze certain types of processes is also described.
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