eCommTouch: A Benchmark Dataset for Touch-based Continuous Mobile Device Authentication for e-Commerce

Masashi Kudo, Tsubasa Takahashi, Isao Echizen, Hayato Yamana

Abstract: With the increasing use of smartphones for accessing e-commerce (EC) websites, the need for supplementary post-login continuous authentication has grown, which results in the need for touch stroke datasets for research. Existing datasets for touch-based authentication, primarily focused on scenarios like reading text or social media, exhibit a directional bias in strokes, limiting their applicability in EC environments where touch interactions involve a wider variety of stroke directions. To address this, we introduce eCommTouch, a new public dataset that simulates touch interactions on EC websites. The eCommTouch dataset includes a diverse set of strokes in leftward, rightward, upward, and downward directions, and provides a larger number of strokes per session than existing datasets. Our preliminary evaluation using equal error rate (EER) on the eCommTouch dataset revealed that stroke direction-specific classifiers enhance authentication performance, i.e., lowering EER, when a user has nearly balanced strokes in each direction, such as leftward and rightward, which has not been confirmed with existing datasets. We anticipate that the eCommTouch dataset will contribute to advancing touch-based authentication, particularly in enhancing security for EC platforms by leveraging diverse stroke patterns. The eCommTouch dataset is available from https://github.com/yamanalab/eCommTouch.