DoubleStrokeNet: Bigram-Level Keystroke Authentication

Abstract

Keystroke authentication is a well-established biometric technique that has gained significant attention due to its non-intrusive and continuous characteristics. The method analyzes the unique typing patterns of individuals to verify their identity while interacting with the keyboard, both virtual and hardware. Current deep-learning approaches like TypeNet and TypeFormer focus on generating biometric signatures as embeddings for the entire typing sequence. The authentication process is defined using the Euclidean distances between the new typing embedding and the saved biometric signatures. This paper introduces a novel approach called DoubleStrokeNet for authenticating users through keystroke analysis using bigram embeddings. Unlike conventional methods, our model targets the temporal features of bigrams to generate user embeddings. This is achieved using a Transformer-based neural network that distinguishes between different bigrams. Furthermore, we employ self-supervised learning techniques to compute embeddings for both bigrams and users. By harnessing the power of the Transformer’s attention mechanism, the DoubleStrokeNet approach represents a significant departure from existing methods. It allows for a more precise and accurate assessment of user authenticity, specifically emphasizing the temporal characteristics and latent representations of bigrams in deriving user embeddings. Our experiments were conducted using the Aalto University keystrokes datasets, which include 136 million keystrokes from 168,000 subjects using physical keyboards and 63 million keystrokes acquired on mobile devices from 60,000 subjects. The DoubleStrokeNet outperforms the TypeNet-based authentication system using 10 enrollment typing sequences, achieving Equal Error Rate (EER) values of 0.75% and 2.35% for physical and touchscreen keyboards, respectively.