# Authentication by Keystroke Dynamics: The Influence of Typing Language
## 4 Results and Discussion
### Performance Evaluation
#### 4.2.2 Bksd--Phrase  
Tables 7-10 present the average results (of 35 users) achieved by each of the four classifiers on the phrase dataset by training and testing on Arabic, training and testing on English, training on Arabic and testing on English, and training on English and testing on Arabic, respectively. Tables 7 and 8 show similar results to the password dataset: when a classifier is trained and tested on the same language, it achieves excellent results and is able to distinguish imposters with minimal authentic user rejection. Again, AdaBoost and random forest outperform decision tree and SVM, although their performance is excellent as well. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Cls** & **EER** & **ZM-FAR** & **R** & **P** & **Acc** & **FRR** & **FAR** \\ \hline AdaB & 0.490 & 0.537 & 0.985 & 0.499 & 0.501 & 0.964 & 0.015 \\ DT & 0.488 & 0.988 & 0.848 & 0.507 & 0.510 & 0.824 & 0.152 \\ RF & 0.483 & 0.707 & 0.917 & 0.511 & 0.514 & 0.884 & 0.083 \\ SVM & 0.471 & 0.960 & 0.787 & 0.522 & 0.528 & 0.728 & 0.213 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Average results achieved by each of the four classifiers on the phrase dataset when training on English and testing on Arabic. Cls: classifier; R: recall; P: precision; Acc: accuracy; AdaB: AdaBoost. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cls** & **EER** & **ZM-FAR** & **R** & **P** & **Acc** & **FRR** & **FAR** \\ \hline AdaB & 0.011 & 0.211 & 0.987 & 0.991 & 0.989 & 0.009 & 0.013 \\ DT & 0.096 & 0.948 & 0.897 & 0.912 & 0.904 & 0.090 & 0.103 \\ RF & 0.043 & 0.460 & 0.947 & 0.967 & 0.957 & 0.033 & 0.053 \\ SVM & 0.088 & 0.682 & 0.896 & 0.926 & 0.911 & 0.072 & 0.104 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average results achieved by each of the four classifiers on the phrase dataset when training and testing on English. Cls: classifier; R: recall; P: precision; Acc: accuracy; AdaB: AdaBoost. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cls** & **EER** & **ZM-FAR** & **R** & **P** & **Acc** & **FRR** & **FAR** \\ \hline AdaB & 0.014 & 0.196 & 0.983 & 0.989 & 0.986 & 0.011 & 0.017 \\ DT & 0.114 & 0.983 & 0.869 & 0.899 & 0.886 & 0.097 & 0.131 \\ RF & 0.051 & 0.454 & 0.948 & 0.950 & 0.949 & 0.051 & 0.052 \\ SVM & 0.106 & 0.676 & 0.886 & 0.901 & 0.894 & 0.097 & 0.114 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average results achieved by each of the four classifiers on the phrase dataset when training and testing on Arabic. Cls: classifier; R: recall; P: precision; Acc: accuracy; AdaB: AdaBoost. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cls** & **EER** & **ZM-FAR** & **R** & **P** & **Acc** & **FRR** & **FAR** \\ \hline AdaB & 0.482 & 0.841 & 0.958 & 0.511 & 0.514 & 0.922 & 0.042 \\ DT & 0.472 & 0.986 & 0.867 & 0.523 & 0.528 & 0.811 & 0.133 \\ RF & 0.463 & 0.864 & 0.934 & 0.531 & 0.538 & 0.860 & 0.066 \\ SVM & 0.458 & 0.967 & 0.785 & 0.540 & 0.543 & 0.700 & 0.215 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Average results achieved by each of the four classifiers on the phrase dataset when training on Arabic and testing on English. Cls: classifier; R: recall; P: precision; Acc: accuracy; AdaB: AdaBoost. Tables 9 and 10 also show that when classifiers are trained on a different language than the testing language, the classifier performance deteriorates, with accuracies close to random guessing. In addition, the performance of Arabic trained and English tested slightly exceeds that of English trained and Arabic tested. The phrase experiment further corroborates the findings from the password experiment: the user's typing pattern is different when the user switches languages. The high recall and low precision values in Tables 5, 6, 9 and 10 indicate that the classifiers identify most cases as attackers, rejecting many authentic users, and they are correct about half of the time. This corresponds with the high FRR and low FAR values: the classifiers reject authentic users whilst still identifying intruders.