## 4 Results and Discussion  
The primary objective of this study was to classify users based on their mother tongue using keystroke durations as features and machine learning models. To evaluate the performance of these models, metrics such as accuracy, time to build model, F1 score, and area under the ROC curve (AUC) were utilised.  
The AUC was chosen as a relevant metric for evaluating the model's performance, particularly when dealing with imbalanced classes. The AUC quantifies the area under the ROC curve, which illustrates the trade-off between the true and false positive rates at various classification thresholds.  
Each experiment was performed with the 10-fold cross-validation method to obtain a more reliable statistical measure since it is performed ten times with a different training set and testing set each time, and their average value is calculated. This avoids the possibility of calculating an outlier as accuracy, F1 score and AUC.  
Through a series of extensive experiments, the best results obtained from each classifier were documented in Table 2.  
The best results of Table 2 were obtained for the SVM model having a C parameter value equal to 10, for the RBFN model having 10 clusters and a minimum standard deviation for clusters equal to 1.5, for the RF model having 1100 trees and 150 features to be considered in random feature selection, and for the MLP model having a learning rate of 0.6 and a momentum of 0.8.  
Figure 1 shows the best results of the five machine learning models in searching the mother tongue of unknown users.  
Based on the findings, the RBFN model demonstrated the highest accuracy of 82.5% and an F1 score of 0.823, followed by the MLP model with 81.4% accuracy and 0.814 F1 score. Conversely, MLP had the highest value of 0.946 in terms of AUC, followed by RBFN with 0.939. Notably, the NB and SVM models were observed to have shorter training times compared to other models.  
Two conclusions can be drawn from these results. Firstly, the classification of users based on their mother tongue using KD data is feasible, with an accuracy of over 82% achieved in this dataset, surpassing the baseline accuracy of around 28%. Secondly, RBFN exhibited the best overall performance in accuracy and F1 score, but SVM may be more practical for real-world applications due to its shorter training time.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Accuracy** & **Time to Build (in s)** & **F1 Score** & **AUC** \\ \hline SVM & 75.8\% & 0.12 & 0.757 & 0.884 \\ NB & 66.5\% & 0.01 & 0.652 & 0.878 \\ RBFN & 82.5\% & 0.31 & 0.823 & 0.939 \\ RF & 77.3\% & 16.25 & 0.767 & 0.926 \\ MLP & 81.4\% & 11.18 & 0.814 & 0.946 \\ \hline \end{tabular}
\end{table}
Table 2: The best results of each of SVM, NB, RBFN, RF, and MLP models, in terms of accuracy, time to build model (in s), F1 score, and area under the ROC curve.  
Finally, a closer examination of the results is presented in the confusion matrix shown in Table 3, which corresponds to the best-performing run of the experiments.  
Although the languages under investigation belong to distinct language families, as elucidated earlier, Table 3 presents evidence indicating a higher likelihood of misclassification of logfiles as belonging to languages spoken in geographically adjacent regions. For instance, log files attributed to the Albanian language were occasionally misclassified as Greek or Bulgarian, which are official languages of neighbouring countries. While this observation is a preliminary finding, it merits further investigation in an extended research study to better comprehend the underlying factors contributing to this phenomenon.  
Since there is no other research in the literature that attempts to identify the mother tongue of users using KD, the results of this research cannot be directly compared with those of any other.