Inverse Biometrics for Mouse Dynamics

Abstract

Various techniques have been proposed in different literature to analyze biometric samples collected from individuals. However, not a lot of attention has been paid to the inverse problem, which consists of synthesizing artificial biometric samples that can be used for testing existing biometric systems or protecting them against forgeries. In this paper, we present a framework for mouse dynamics biometrics synthesis. Mouse dynamics biometric is a behavioral biometric technology, which allows user recognition based on the actions received from the mouse input device while interacting with a graphical user interface. The proposed inverse biometric model learns from random raw samples collected from real users and then creates synthetic mouse actions for fake users. The generated mouse actions have unique behavioral properties separate from the real mouse actions. This is shown through various comparisons of behavioral metrics as well as a Kolmogorov–Smirnov test. We also show through a two-fold cross-validation test that by submitting sample synthetic data to an existing mouse biometrics analysis model we achieve comparable performance results as when the model is applied to real mouse data.

Inverse biometrics for keystroke dynamics

“…One of the important benefits of using keystroke dynamics (as well as other behavioural biometrics) is that the False Rejection Rate (FRR) and the False Alarm Rate (FAR) -two parameters characterizing the performance of such systems -can be adjusted by changing the acceptance threshold at the individual level [1]. This adjustment mechanism allows the system to explicitly define individual risk mitigation -a feature that traditional biometric technologies can never achieve [ 4]. Another benefit of using keystroke dynamics is that they can be captured continuously, i.e., not just at the start-up time and they can accurately be used to trigger an alarm to another system or person.…”

“…Inverse biometric consists of the synthesis of artificial biometric samples that can be used for testing existing biometric systems or protecting them against forgeries [15]. Biometric synthesis is the inverse problem of biometric analysis, which involves collecting and processing biometric samples from human users and then, simulating these samples to achieve the same purpose [ 4]. Our proposed novel inverse biometric model is able to generate as much data as required that mimic the behaviours of real human-like data, while being as much accurate as possible.…”

User Identification Based on Touch Dynamics

“…Several studies document mouse dynamics with biometric applications [9][10][11][12] and these have many similarities to touch dynamics. Features studied have included, mouse travel distance in pixels, travel time and direction [11]. One study on mouse dynamics concluded that users can be successfully identified, but recommend that identification is done in combination with other measures [12].…”

Behavioural Biometrics for Multi-Factor Authentication in Biomedicine

“…When collecting the actions, several factors have to be taken into account because they can affect the accuracy of the analysis of the mouse biometric samples. These factors are listed below [14]:…”