Kolmogorov-Smirnov test for keystroke dynamics based user authentication

Ceffer, Attila; Levendovszky, János

doi:10.1109/cinti.2016.7846387

Cited by 3 publications

(1 citation statement)

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“…Other statistical methods used for classification of keystroke dynamics in literature includes Euclidean distance [24], scaled Euclidean distance [25], scaled Manhattan distance with Mean of Horners Rules [26], Mahalanobis distance [21], and Bhattacharyya distance with Gaussian mixture model [27]. In addition, other statistical techniques such as Hidden Markov Model [28], Kolmogorov-Smirnov Test (KS-test) [29] , and Bayesian Classification [30], were also employed to find the level of similarity between keystroke samples. It has been noticed that most of the research works in CUA domain had considered the block of actions to authenticate the user.…”

Section: Literature Reviewmentioning

confidence: 99%

Continuous User Authentication Featuring Keystroke Dynamics Based on Robust Recurrent Confidence Model and Ensemble Learning Approach

et al. 2020

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User authentication is considered to be an important aspect of any cyber security program. However, one-time validation of user's identity is not strong to provide resilient security throughout the user session. In this aspect, continuous monitoring of session is necessary to ensure that only legitimate user is accessing the system resources for entire session. In this paper, a true continuous user authentication system featuring keystroke dynamics behavioural biometric modality has been proposed and implemented which tends to authenticate the user on each action. The proposed 2-phase methodology, consisting of ensemble learning and robust recurrent confidence model, decides the genuineness of user based on each action and locks out the imposter user based on final threshold, however, it tends to lock out imposter user more quickly if it reaches the alert threshold. Moreover, system has been validated with two different experimental settings and results are reported in terms of mean average number of genuine actions (ANGA) and average number of imposter actions(ANIA), whereby achieving the lowest mean ANIA with experimental setting II.

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