Wrist in motion: A seamless context-aware continuous authentication framework using your clickings and typings

In this paper, we propose enhanced continuous authentication by supplementing keystroke and mouse dynamics with wrist motion behaviors. Our method bridges the security gap when neither the mouse nor the keyboard is being used, such as during transitions from mouse to keyboard and vice versa, or during intermittent pauses when wrist movement is captured. Context-aware keystroke latency feature cell generation improves performance and solves latency fluctuation-different diagraphs have different latencies in different words. Based on two Random Forest Ensemble Classifiers (RFECs) recognizing the mouse and keystroke actions with corresponding wrist motions and one Sequential Sampling Analysis (SSA) or SSA Dynamic Trust Model (SSA-DTM), the identity of the user can be continuously verified no matter the operation mode-mouse clicking or keyboard typing. Experimental results, based on 44 subjects, show that the proposed approach can reach an FRR of 0.92% for genuine users and an FAR of 0 for attackers. The approach is shown to be more superior in efficient and timely authentications by making an authentication decision within only 35 mixed actions-mouse clicks or keystrokes, compared with conventional methods solely based on the mouse geometry and locomotion features or keystroke latency features.