Securing Account Recovery Mechanism on Desktop Computers and Mobile Phones with Keystroke Dynamics

Wahab, Ahmed Anu; Hou, Daqing; Schuckers, Stephanie; Barbir, Abbie

doi:10.1007/s42979-022-01245-3

Cited by 5 publications

(2 citation statements)

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“…Future work also includes implementing a deep learning framework for multi-user classification, the investigation of typing behavior with time, and how that can affect the performance of this and similar classification systems. Further exploration is warranted to modify this algorithm for use in datasets such as Clarkson Account Recovery dataset [45]. Other distance measures such as the Mahalanobis distance can be investigated for their effectiveness when used as part of the proposed algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…We would also like to point out that this type of work is relatively unaddressed in literature, and we were only able to find one paper addressing this very important issue [24]. We would also like to note that relatively less work has been done with Clarkson Account Recovery Dataset [45], and therefore, we will not discuss it here, but leave it for future exploration.…”

Section: Related Workmentioning

confidence: 99%

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A novel non-linear transformation based multi-user identification algorithm for fixed text keystroke behavioral dynamics

Sahu¹,

Banavar²,

Schuckers³

2022

Preprint

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In this paper, we propose a new technique to uniquely classify and identify multiple users accessing a single application using keystroke dynamics. This problem is usually encountered when multiple users have legitimate access to shared computers and accounts, where, at times, one user can inadvertently be logged in on another user's account. Since the login processes are usually bypassed at this stage, we rely on keystroke dynamics in order to tell users apart. Our algorithm uses the quantile transform and techniques from localization to classify and identify users. Specifically, we use an algorithm known as ordinal Unfolding based Localization (UNLOC), which uses only ordinal data obtained from comparing distance proxies, by "locating" users in a reduced PCA/Kernel-PCA/t-SNE space based on their typing patterns. Our results are validated with the help of benchmark keystroke datasets and show that our algorithm outperforms other methods.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%