# Authentication by Keystroke Dynamics: The Influence of Typing Language
## 4 Results and Discussion
### Performance Evaluation
#### 4.2.3 Bigram Keystroke Dynamics  
In this section, we present the results of testing a segment of BKSD based on bigram frequencies. Table 2 shows the most frequent bigrams in both languages present in our dataset. The most frequent English bigram is \"th\" and the corresponding Arabic letters on the keyboard give the bigram \"\(\dot{\textbf{i}}\)\", which is the 88 th most frequent bigram in Arabic. The third most frequent Arabic bigram is present in our dataset: \"\(\textbf{Y}\)\", and the corresponding English bigram \"gh\" does not appear in our dataset. We design a simple experiment where we compare the classifier performance on a sub-dataset composed of the most frequent bigram in English, \"th\", and the third most frequent bigram in Arabic, \"\(\textbf{Y}\)\", as well as the most frequent English bigram, \"th\", and its corresponding Arabic bigram, \"\(\dot{\textbf{i}}\)\", and report the results in this section. We do not select bigrams with the same key positions, in order to test the system's ability to verify users based on frequent bigrams. Tables 11-14 present the average results achieved by each of the four classifiers by training and testing on Arabic, training and testing on English, training on English, training on Arabic and testing on English, and training on English and testing on Arabic, respectively. As expected, due to the restricted number of features used in training, the classifiers' accuracy deteriorates slightly when training and testing on the same language due to underfitting. In addition, the classifiers continue to exhibit an inability to differentiate users based on them typing a very frequent bigram in English with a very frequent bigram in Arabic, as well as when comparing the frequent English bigram and its corresponding (positional) Arabic bigram. The study by [PERSON] et al. [7] corroborates our findings, 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, while on our dataset, an average EER of 0.486% was achieved when enrolling in English and testing on Arabic, and 0.475% when enrolling in Arabic and testing on English. A direct comparison of results between the two studies is not beneficial. The two studies differ in languages used, the dataset itself and the dataset collection approach (static vs. free-text), the familiarity of the user with the keyboard, as well as the algorithms used to assess the dataset. The deterioration of EER in our dataset might be caused by users having to use an unfamiliar keyboard compared with users using their own keyboard in [7]. Allowing users to type on their own keyboards might have inflated the EER results. Conversely, the discrepancy may be attributed to language-specific issues or simply to the differences in the algorithms used. More study is needed to conclusively answer this question. In summary, the results show that users typing in English and Arabic have different typing patterns for each language, and each person has their own distinct typing rhythms that are language dependent. When a classifier is trained on one language, the user's own typing pattern or keystroke dynamics in another language is rejected as an attacker. This signifies that other factors affect a person's keystroke dynamics, factors that are