# Authentication by Keystroke Dynamics: The Influence of Typing Language
## 1 Introduction  
Identity verification is of paramount importance, especially considering the highly connected nature of today's world that allows remote access to data, information, and user accounts. One approach for user verification is to employ keystroke dynamics for authentication or identification. Authentication aims to show that the user is the actual legitimate user of the system, while identification aims to identify the user of the system. Keystroke dynamics is a behavioural biometric that can be used for both user authentication and identification. The timing information of a user while typing on a keyboard is measured, and then a suitable algorithm is chosen for user verification [1]. Thus, keyboard dynamics is a non-intrusive method that does not require any specialised hardware, making it practical and cost effective. It is an attractive option for security, especially considering that although a password may be known by an attacker, emulating the typing pattern of the user is difficult [2]. Conversely, as a behavioural biometric and not a physical biometric, e.g., a fingerprint, keyboard dynamics can be affected by a wide range of factors, such as the emotional state of the user, illness or lack of sleep, and so on [3]. Practical applications of keystroke dynamics should. Keystroke dynamics can be used for the authentication of users on a system, as well as for detecting intrusions, either in an offline or online setting. User usage modelling is another area that may benefit from keystroke dynamics in order to develop systems that present services or advertisements appropriate to the user. Illegal activities may also be detected by keystroke dynamics, for example, when typing from anonymous accounts is detected on the web, it can be used to flag suspicious activity. Another area is the use of keystroke dynamics in forensics and surveillance, as regardless of the number of machines used, a criminal still retains their typing pattern. A recent survey [4] presented a comprehensive review and analysis of keystroke dynamics systems. Researchers can collect many different features from the typing patterns of users. The most widely used is keydown-keydown latency, which is the time taken for a user to presstwo consecutive keys [2; 5]. It may be divided into two timing measures: keydown-keyup of the first key, known as the hold-time or dwell-time, and keyup-keydown of the first and second keys, known as the flight-time; see Figure 1. Timings involving more than two consecutive keys can also be measured, although they are less common in the literature, as are other measures such as collecting data on key pressure, typing speed, total duration and typing errors [5]. Using these features, a system may either be an anomaly detection system or a classification system. An anomaly detection system is one that is trained on the examples generated by the user, and when a new sample needs to be tested, it is compared to the model of the user saved in the system. The second approach is to use classification, where a system is trained on two or more users, and a new sample is classified into a user category during testing. Based on the literature, one can differentiate between static-text and dynamic-text keystroke dynamics [5]. In the former, the user enters a predefined string of text, and the system verifies the user at the initial interaction with the system, such as when a user enters a password or a pin code. The latter attempts to verify a user that has already gained access to the system during the period the user is interacting with the system. In other words, it is continuously re-authenticating the user. Although approximately 43% of people worldwide are bilingual [6], there is a lack of bilingual keystroke dynamics research. [PERSON] et al. [7] studied the effects of using different keyboards with different language settings. They developed two free-text datasets, one using different keyboard types, and the other for bilingual English and Chinese speaking users.