![]() ![]() ![]() Thus, security or privacy breaches are a major challenge. Because of the increasing dependence on information systems, immense amounts of personal and sensitive data are stored on mobile devices. text without appendices, 23 pictures, 13 tables and 28 bibliographical entries.Ībstract Diverse applications are used on mobile devices. The paper describes the concept of passwords and password cracking using classical methods and data-driven methods which use password datasets or dictionaries.Īlso, in the paper, important characteristics of passwords were described and included in the final dataset as features.Įach instance in the dataset represents a unique password that was generated randomly by using four suggested password collection generation approaches based on statistical analysis or collected from other sources. In this master thesis paper, a method for password dataset generation is proposed and evaluated for use in machine learning and password cracking. Unlike dictionaries, password datasets should contain data (numeric In the area of password cracking, specifically, password dictionary and password dataset are used interchangeably but actually mean two different things. Machine learning is a widely used in many different fields but its usage in password analysis and password cracking is very low and limited to password strength classification and is based on very simplified dataset of two columns. These results encourage the deployment of our proposed approach in comparison to traditional PIN and OTP systems where the attack would have 100% success rate under the same impostor scenario. 4.0% when the attacker knows the password. Finally, we discuss specific details for the deployment of our proposed approach on current PIN and OTP systems, achieving results with Equal Error Rates (EERs) ca. This database is used in the experiments reported in this work and it is available together with benchmark results in GitHub. The new e-BioDigit database, which comprises on-line handwritten digits from 0 to 9, has been acquired using the finger as input on a mobile device. A complete analysis of our proposed biometric system is carried out regarding the discriminative power of each handwritten digit and the robustness when increasing the length of the password and the number of enrolment samples. In our proposed approach, users draw each digit of the password on the touchscreen of the device instead of typing them as usual. This work enhances traditional authentication systems based on Personal Identification Numbers (PIN) and One-Time Passwords (OTP) through the incorporation of biometric information as a second level of user authentication. ![]()
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