Sequence Alignment with Dynamic Divisor Generation for Keystroke Dynamics Based User Authentication

Abstract

Keystroke dynamics based authentication is one of the prevention mechanisms used to protect one’s account from criminals’ illegal access. In this authentication mechanism, keystroke dynamics are used to capture patterns in a user typing behavior. Sequence alignment is shown to be one of effective algorithms for keystroke dynamics based authentication, by comparing the sequences of keystroke data to detect imposter’s anomalous sequences. In previous research, static divisor has been used for sequence generation from the keystroke data, which is a number used to divide a time difference of keystroke data into an equal-length subinterval. After the division, the subintervals are mapped to alphabet letters to form sequences. One major drawback of this static divisor is that the amount of data for this subinterval generation is often insufficient, which leads to premature termination of subinterval generation and consequently causes inaccurate sequence alignment. To alleviate this problem, we introduce sequence alignment of dynamic divisor (SADD) in this paper. In SADD, we use mean of Horner’s rule technique to generate dynamic divisors and apply them to produce the subintervals with different length. The comparative experimental results with SADD and other existing algorithms indicate that SADD is usually comparable to and often outperforms other existing algorithms.