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Account recovery is ubiquitous across web applications but circumvents the username/password-based login step. Therefore, it deserves the same level of security as the user authentication process. A common simplistic procedure for account recovery requires that a user enters the same email used during registration, to which a password recovery link or a new username could be sent. Therefore, an impostor with access to a user’s registration email and other credentials can trigger an account recovery session to take over the user’s account. To prevent such attacks, beyond validating the email and other credentials entered by the user, our proposed recovery method utilizes keystroke dynamics to further secure the account recovery mechanism. Keystroke dynamics is a type of behavioral biometrics that uses the analysis of typing rhythm for user authentication. Using a new dataset with over 500,000 keystrokes collected from 44 students and university staff when they fill out an account recovery web form of multiple fields, we have evaluated the performance of five scoring algorithms on individual fields as well as feature-level fusion and weighted-score fusion. We achieve the best EER of 5.47% when keystroke dynamics from individual fields are used, 0% for a feature-level fusion of five fields, and 0% for a weighted-score fusion of seven fields. Our work represents a new kind of keystroke dynamics that we would like to call it ‘medium fixed-text’ as it sits between the conventional (short) fixed text and (long) free text research.
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This content will become publicly available on July 1, 2026
Typing in tandem: Language planning in multisentence text production is fundamentally parallel.
Classical serial models view the process of producing a text as a chain of discrete pauses during which the next span of text is planned, and bursts of activity during which this text is output onto the page or computer screen. In contrast, parallel models assume that by default planning of the next text unit is performed in parallel with previous execution. We instantiated these two views as Bayesian mixed-effects models across six sets of keystroke data from child and adult writers composing different types of multi-sentence text. We modelled interkey intervals with a single distribution, hypothesised by the serial processing account, and with a two-distribution mixture model that is hypothesised by the parallel-processing account. We analysed intervals occuring before-sentence, before word, and within word. Model comparisons demonstrated strong evidence in favour of the parallel view across all datasets. When pausing occurred, sentence initial inter-keystroke intervals were longer than word initial pauses. This is consistent with the idea that edges of larger linguistic units are associated with higher level planning. However, we found – across populations – that interkey intervals at word and even at sentence boundaries were often too brief to plausibly represent time to plan what was written next. Our results cannot be explained by the serial processing but are in line with the parallel view of multi-sentence text composition.
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- Award ID(s):
- 2302644
- PAR ID:
- 10614280
- Publisher / Repository:
- APA
- Date Published:
- Journal Name:
- Journal of Experimental Psychology: General
- Volume:
- 154
- Issue:
- 7
- ISSN:
- 0096-3445
- Page Range / eLocation ID:
- 1824 to 1854
- Subject(s) / Keyword(s):
- parallel processing writing mixture models language production keystroke logging
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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