# Authentication by Keystroke Dynamics: The Influence of Typing Language
## 1 Introduction
The bilingual dataset was collected using the user's personal keyboard, and was evaluated using two free-text algorithms, the ITAD metric [8] and D-Vectors model [9]. They reported that the use of different languages significantly affected the keystroke dynamics system's performance, with an average EER of 0.210% when enrolling in English and testing on Chinese, and 0.253% when enrolling in Chinese and testing on English. For a bilingual user using a system employing dynamic-text keyboard dynamics, and in situations with high security requirements, the authentication system must be able to re-authenticate the user in the event the user switches their typing language. A system must not flag the user as an imposter simply for changing the typing language. For this reason, we study the impact of language as a factor that may affect keystroke dynamics. Specifically, in this paper, we are interested in answering the question of whether the keystroke dynamics of a user are dependent on the spatial distances between letters on the keyboard, such that the user's typing pattern is the same if the same keyboard is used, regardless of typing language, or whether the keystroke patterns for the same user are language dependent, and a system cannot verify a user enrolled in a language if the user switches to another. In order to answer our query, we study the effect of the typing language on bilingual users' keystroke dynamics. In particular, we collect a static-text dataset for English and Arabic speakers composed of a short password and a phrase, in  
Figure 1: Hold and flight times. both languages, and evaluate our dataset using four classification algorithms. As much as possible, we control the factors that influence keystroke dynamics. For example, we use one mode of data entry: a keyboard that is used by all participants, rather than the user's own keyboard as in [PERSON] et al. [7]. A user will be more familiar when using their own keyboard rather than a keyboard provided at work or school, and in large corporations with high security requirements, it is expected that the user may access the system from various locations. In addition, the aforementioned work was concerned with free-text entry, while in this work, we study the effects on static-text entry. This paper expands the frontier on bilingual keystroke dynamics by presenting the results of bilingual users that use static-text entry as well as using a single keyboard, and is the first work of its kind to deal with bilingualism with the Arabic language. Furthermore, this work provides an answer to the question posed above: whether the spatial arrangement of letters on a keyboard is the primary factor that influences the keystroke dynamics of people and results in unique typing patterns, or whether a person would type differently depending on the language. If the former is true, then a classification system trained on one language and tested on another language for the same user should have similar results. If, however, the typing patterns are affected by other factors, such as the language itself or familiarity with the typing sequences of the most used letters or \(n\)-grams in languages--making a user more proficient at typing those sequences--then a person would not be able to replicate their typing patterns when typing in another language. The contributions of this study are as follows: (1) a static-text bilingual dataset in English and Arabic in two parts (password and phrase) from 85 total users. This dataset is publicly available to researchers, and is the first of its kind in the field of bilingual keystroke dynamics for English/Arabic text, and the first of its kind for Arabic static-text. (2) Whether the typing language affects keystroke dynamics. This is an important consideration for security systems, especially in cases when users speak two or more languages. (3) The expected deterioration in EER when using an unfamiliar keyboard. This is discussed at length and compared with previous work in Section 4.2.3. Finally, (4) some interesting areas for future work.