# Authentication by Keystroke Dynamics: The Influence of Typing Language
## 2 Literature Review  
Most research on keystroke dynamics is focused on fixed- or static-text entry on computer keyboards. Recent trends in the literature, however, are more concerned with modern technology, including mobile devices [10]. Mobile devices introduce a number of new and different challenges compared with traditional keyboards [11], such as limited computational resources, functions such as predictive text, and keyboard sizes that affect typing patterns: typing with one or two hands, or just a finger, by the same user [10]. Existing research shows that statistical classification methods [12] as well as machine learning methods [13] can be used for the verification of users; however, the results fail to meet secure verification requirements [10]. The most used algorithms are support-vector-based classifiers and tree-based classifiers, including random forest, AdaBoost, and decision trees, all of which produce results with impressive accuracy [4]. Machine learning methods have been shown to outperform traditional methods when it comes to the accuracy of predictions [14]. Many factors influence keystroke dynamics. We can divide these factors into two categories: internal and external. Internal factors are those that are personal to the user, for example, emotional state, language, or amount of sleep. External factors are those that involve the hardware being used, such as keyboard type or computer type. We detail a number of studies that have dealt with external factors next. A study by [PERSON] [15] collected a dataset using a desktop computer and hand-held devices, and found that different keyboards produced different results using the log-logistic distribution. These results corroborate other studies that found differences between personal computer keyboards and laptop keyboards [7]. In addition, the varying size of screens on handheld devices leads to changing system performance in various measures, such as accuracy and acceleration [16]. The system clock resolution also affects the detector's performance, with higher clock speeds achieving better performance [17]. The use of additional features obtained from specialised sensors available on smart phones has shown improved performance for keystroke dynamics user recognition, for example, pressure sensors [18], accelerometers [19], and gyroscopes [20]. As mentioned previously, keystroke dynamics are affected by factors internal to the user. [PERSON] and [PERSON] [21] performed gender, age, and handedness classifications in social networks using keystroke dynamics. Another study by [PERSON] et al. [22] attempted to profile the user's mother tongue based on their typing patterns for the Albanian, Bulgarian, English, Greek, and Turkish languages. The use of keystroke dynamics for emotion recognition was studied by [PERSON] et al. [23] to detect stress in users via data collected from accelerometers and gyroscopes using smartphones. Indeed, keystroke dynamics has been used to identify many emotional states, both positive emotions such as happiness, confidence and excitement, as well as negative emotions such as anger, sadness and fear, and a recent review by [PERSON] and [PERSON] [24] surveyed papers presenting many more emotional states. [PERSON] et al. [25] showed that keystroke dynamics can be used to classify user experience. A study by [PERSON] et al. [26] combined gait characteristics from smartphones' accelerometers to improve the accuracy of keystroke dynamics systems. The previous studies were all concerned with factors that affect keystroke dynamics. However, keystroke dynamics was found to be helpful in other fields such as medical, human-computer interaction (HCI), as well as the most well-known field, security. In the medical field, a number of studies have used keystroke dynamics for disease detection, such as the diagnosis of fine motor decline [27] and Parkinson's disease [28]. Keystroke dynamics has also been used to detect fatigue [29]. In the field of HCI, a study by [PERSON] et al.