Katsuya Matsuoka
ABSTRACT
Recently, information security has attracted more interest from researchers. Personal authentication has become more important than ever, because authentication vulnerability is regarded as a problem. In cases where such high confidentiality is required, multi-factor authentication which combines multiple authentication factors is often used. In this study, we focus on score fusion method which merge authentication score of each factor in multi-factor authentication. In conventional score fusion methods, the weighting of factors is fixed. Therefore, they are not suitable when the tendency for factors of high accuracy is different between users. We propose a user dependent weighting score fusion method using neural network. Our proposed method is evaluated in comparison with conventional score fusion methods. The result shows that the accuracy of our proposed method is higher than conventional methods.
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Index Terms
- A Score Fusion Method by Neural Network in Multi-Factor Authentication
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