Abstract
Cybersecurity companies from around the world use state-of-the-art technology to provide the best protection against malicious software. Recent times have seen behavioral biometry becoming one of the most popular and widely used components in MFA (Multi-Factor Authentication). The effectiveness and lack of impact on UX (User Experience) is making its popularity rapidly increase among branches in the area of confidential data handling, such as banking, insurance companies, the government, or the military. Although behavioral biometric methods show a high degree of protection against fraudsters, they are susceptible to the quality of input data. The selected behavioral biometrics are strongly dependent on mobile phone IMU sensors. This paper investigates the harmful effects of gaps in data on the behavioral biometry model’s accuracy in order to propose suitable countermeasures for this issue.
Funder
Development and verification of new methods of user authentication based on behavioral biometrics and machine learning methods
European Regional Development Fund
Statutory Research for Young Researchers funds
Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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