Publications
カンファレンス (国際) Effective Clustering of Large-Scale Data for Multi-modal Behavioral Authentication
Shuji Yamaguchi, Hidehito Gomi, Ryosuke Kobayashi (The University of Tokyo), Tran Phuong Thao (The University of Tokyo), Mhd Irvan (The University of Tokyo), Rie Shigetomi Yamaguchi (The University of Tokyo)
The 15th Asia Joint Conference on Information Security (AsiaJCIS 2020)
2020.8.21
We propose an effective classification algorithm for machine learning to achieve higher performance for multi-modal behavioral authentication systems. Our algorithm uses a multiclass classification scheme that has a smaller number of classes than the number of users stored in the dataset. We also propose metrics, the self-mix-classified rate, other-single-classified rate, and equal-classified rate, for use with the proposed algorithm to determine an optimal number of classes for behavioral authentication. We conducted experiments using a large-scale dataset of activity histories that are stored when 10,000 users use commercial smartphone-applications to analyze performance measures such as false rejection rate, false acceptance rate, and equal error rate obtained with our proposed algorithm. The results indicate our algorithm achieved higher performance than that for previous ones.
Paper :
Effective Clustering of Large-Scale Data for Multi-modal Behavioral Authentication
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