Mobile Keystroke Biometrics Using Transformers
Giuseppe Stragapede, Paula Delgado-Santos, Ruben Tolosana and, Ruben Vera-Rodriguez, Richard Guest, Aythami Morales

TL;DR
This paper introduces a novel keystroke biometric system based on Transformer models, achieving superior accuracy in mobile user authentication by addressing challenges in free-text scenarios.
Contribution
First to propose Transformer-based architecture for keystroke biometrics, significantly improving accuracy over existing deep learning methods.
Findings
Achieved 3.84% EER on Aalto database
Outperformed state-of-the-art approaches
Effective with only 5 enrollment sessions
Abstract
Among user authentication methods, behavioural biometrics has proven to be effective against identity theft as well as user-friendly and unobtrusive. One of the most popular traits in the literature is keystroke dynamics due to the large deployment of computers and mobile devices in our society. This paper focuses on improving keystroke biometric systems on the free-text scenario. This scenario is characterised as very challenging due to the uncontrolled text conditions, the influence of the user's emotional and physical state, and the in-use application. To overcome these drawbacks, methods based on deep learning such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been proposed in the literature, outperforming traditional machine learning methods. However, these architectures still have aspects that need to be reviewed and improved. To the best of our…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
Mobile Keystroke Biometrics Using Transformers· youtube
Taxonomy
TopicsUser Authentication and Security Systems · Hand Gesture Recognition Systems · Emotion and Mood Recognition
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Softmax · Absolute Position Encodings · Dropout · Byte Pair Encoding · Adam · Residual Connection · Label Smoothing
