INVERSE BIOMETRICS FOR MOUSE DYNAMICS

Author:

NAZAR AKIF1,TRAORÉ ISSA1,AHMED AHMED AWAD E.1

Affiliation:

1. Department of Electrical and Computer Engineering, University of Victoria, P.O. Box 3055 STN CSC, Victoria, B.C. V8W 3P6, Canada

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.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Reversing the irreversible: A survey on inverse biometrics;Computers & Security;2020-03

2. Biometric Authentication Using Mouse Gesture Dynamics;IEEE Systems Journal;2013-06

3. Continuous Authentication in Computers;IT Policy and Ethics;2013

4. User Identification Based on Touch Dynamics;2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing;2012-09

5. Improving Mouse Dynamics Biometric Performance Using Variance Reduction via Extractors With Separate Features;IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans;2010-11

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