Spellchecker Analysis for Behavioural Biometric of Typing Errors Scenario

Bartlomiej Marek, Wojciech Wodo

Abstract: Unlike the typical approach using keystroke dynamics for user authentication and identification, we focus on a more inherent characteristic - the pattern of typing mistakes, which are not widely investigated in the literature. The paper presents initial research that enables the selection of an appropriate Python-based spellchecker for detection in behavioural biometrics systems based on static text characteristics: typing errors. Integrating a robust spellchecker into a biometric system based on static features such as errors made during typing can significantly enhance its effectiveness and user experience. The study evaluated seven tools and their combinations, amounting to forty-nine variants. The research is split into two phases. The first one used fewer sentences to filter satisfying the criteria tools, for which, in the second phase, the context was expanded to be able to choose the most appropriate one by using more sentences. The ultimate goal of the research is to create d

External IDs:dblp:conf/secrypt/MarekW24