Digital Fingerprints, ul. Żeliwna 38, 40-599 Katowice, Poland.
Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland.
Sensors (Basel). 2022 Dec 7;22(24):9580. doi: 10.3390/s22249580.
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.
来自世界各地的网络安全公司都采用最先进的技术，为恶意软件提供最佳保护。最近，行为生物识别技术已成为多因素身份验证（MFA）中最受欢迎和广泛使用的组件之一。其有效性和对用户体验（UX）的影响较小，使其在处理机密数据的领域（如银行、保险公司、政府或军队）中的分支机构中迅速普及。虽然行为生物识别方法对欺诈者具有高度的保护作用，但它们容易受到输入数据质量的影响。所选的行为生物识别技术强烈依赖于手机 IMU 传感器。本文研究了数据缺口对行为生物识别模型准确性的有害影响，以便为该问题提出合适的对策。
