One-Class Classification to Continuously Authenticate Users Based on Keystroke Timing Dynamics

摘要

Most current authentication mechanisms rely on static initial verification of the user; however, such authentication mechanisms do not verify user identities on already unlocked systems. Spy Hunter, a continuous authentication mechanism, constantly examines user's keystroke timing dynamics to assess the user's identity. Also, Spy Hunter preserves the privacy of the user by only utilizing the key press timings without storing information regarding which keys were pressed. Specifically, Spy Hunter implements two one-class classifiers, only utilizing genuine user samples for training the model, and mitigates adversarial inclusion of impostor data in the model. The data in this preliminary study consists of timing information from 20 users running Spy Hunter in the background while they used their systems. One-class classification was performed independently for all the users. Experimental results show that in less than 80 characters, Spy Hunter performed with as low as 2.05% False Acceptance Rate (FAR) and 2% False Rejection Rate (FRR).