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- W4319158694 abstract "Passwords are playing a major role for authentication in our daily life. However contemporary passwords are typically either difficult to remember or vulnerable to various attacks. In 2017, Blocki, Blum, Datta and Vempala introduced the concept of human computable passwords as a promising authentication method. The fundamental concerns for designing human computable passwords are their usability and security. So far, the security evaluation on human computable passwords authentication schemes is mainly based on complexity-theoretic analysis. In this paper, we initially investigate the security of human computable passwords against neural network-based adversarial attacks. Specifically, we employ the typical multilayer perceptron (MLP) model to attempt to attack the human computable passwords authentication scheme proposed by Blocki-Blum-Datta-Vempala. We present implementation results and the corresponding analysis as well. Our results imply that it is possible for an MLP to learn a simple function, but is difficult for an MLP to learn piecewise functions well." @default.
- W4319158694 created "2023-02-04" @default.
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- W4319158694 date "2023-01-01" @default.
- W4319158694 modified "2023-10-06" @default.
- W4319158694 title "Towards Evaluating the Security of Human Computable Passwords Using Neural Networks" @default.
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- W4319158694 doi "https://doi.org/10.1007/978-3-031-25659-2_22" @default.
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