Homogeneous physio-behavioral visual and mouse-based biometric

Abstract

In this research, we propose a novel biometric system for static user authentication that homogeneously combines mouse dynamics, visual search capability and short-term memory effect. The proposed system introduces the visual search capability, and short-term memory effect to the biometric-based security world for the first time. The use of a computer mouse for its dynamics, and as an input sensor for the other two biometrics, means no additional hardware is required than the standard mouse. Experimental evaluation showed the system effectiveness using variable or one-time passwords. All of these attributes qualify the proposed system to be effectively deployed as a static authentication mechanism.Extensive experimentation was done using 2740 sessions collected from 274 users. To measure the performance, a computational statistics model was specially designed and used; a statistical classifier based on Weighted-Sum produced an Equal Error Rate (EER) of 2.11%.

Behavior Analysis for Electronic Commerce Trading Systems: A Survey

“…In [209] a user behavior modeling authentication method based on mouse interaction was studied. By designing a random virtual keyboard and analyzing users' cognitive time characteristics, such as searching for keys on the virtual keyboard, a behavior authentication method based on a cognitive time model was established in [307]. A risk authentication model combining keyboard and mouse behaviors was proposed in [308].…”

Analysis of Targeted Mouse Movements for Gender Classification

“…Ahmed et al [3] used neural networks to learn a user's mouse dynamics in a specific environment while performing continuous identity authentication. Hamdy and Traore [15] combined mouse dynamics with cognitive measures of visual search capability and short term memory to create a static user verification system. The system used presented the user with a on-screen keyboard with randomly shoufled characters which the user needed to click with the mouse, and opted to use statistical both simple sum and weighted sum fusion methods.…”