# Machine-Learning-Based Password-Strength-Estimation Approach for Passwords of Lithuanian Context
## 1 Introduction  
Cybersecurity is one of the most important and sensitive topics in the world, as various types of hacking usually have dire consequences. Today, research is being conducted in this area. This includes developing data protection methods or models [1], detecting GDPR violations [2; 3], developing or analyzing cyberattacks [4; 5], security, detecting phishing emails [6], malware detection [7], and many other similar applications. The human factor plays a crucial role in security assurance [8]. For an ordinary user, the biggest challenge is to protect their data, devices, and accounts. This is because we live in a time where important information can be leaked and used against us. To balance data and functionality access and security, trusted user authentication is necessary. Password authentication is the most widely used authentication method [9]. It is easy to use and implement in various systems and is, moreover, inexpensive. Authentication methods can include biometrics [10; 11; 12; 13] (fingerprints, face recognition, eye iris recognition, or blinking speed), smart cards, and others, but password authentication remains the most popular. However, password authentication is also one of the most insecure forms of authentication. This is because users often choose easy-to-remember passwords that are easy to decipher [14]. Strong passwords are one of the fundamental ways that password authentication can be considered secure. Imperishable passwords are essential, especially given the increasing number of cyberattacks and data leaks [15]. Password strength is significantly dependenton the user, but password-strength meters are crucial tools that help users create more secure passwords. Passwords are evaluated using password-strength meters based on various factors, for example, password length, complexity, or comparison to common password lists. Nowadays, there are many different password-strength meters that have been developed. Password-strength-estimation models focus on English passwords by including different dictionaries (popular names, places), common password lists, leaked password lists, or other specific dictionaries. Most of them do not consider the passwords from other languages that could be used in the systems. Usually, statistical-based approaches are used to estimate the strength of a password, without taking into account the language in which the password could be written. Meanwhile, many different investigations can be found to confirm the fact that user passwords depend on the human factor [16; 17] of the user. They are closely related to the language or environment used by the user. The extension of existing tools by providing language-specific data (the language dictionary, common names, passwords) is not enough, as some languages are more complex. For example, the Lithuanian language in comparison to the English language is more difficult morphologically, as the words in Lithuanian have more different forms. Furthermore, the Lithuanian language uses diacritics, which are considered special symbols when estimating the strength of the password. However, in the context of the Lithuanian language, it is not a special symbol. All these facts must be taken into account when evaluating the strength of the password. In this study, the main objective is to adjust the password-strength-estimation model to accommodate the requirements of the Lithuanian language. Existing password-strength-estimation tools analyze the usage of English words in the password. Therefore, if a Lithuanian word is used in the password, the password-strength tools provide a positively too high security level. Thus, it is important to incorporate the Lithuanian language to estimate the strength of Lithuanian user passwords. Due to the complexity of the Lithuanian language, traditional deterministic approaches are not suitable.