Presentation + Paper
27 May 2022 Improving cybersecurity through deep learning on keystroke and mouse dynamics
Author Affiliations +
Abstract
Username, password, and biometrics are three-factor authentication for cybersecurity enhancement. Adding keystroke and mouse biometrics to authentication will definitely improves the cybersecurity. Keystroke dynamics refers to the process of measuring and assessing human’s typing rhythm on digital devices. Keystroke timing information such as digraph, dwell time and flight time are used in our experimental datasets. Mouse dynamics records mouse motion (speed), left-, right-, or double-clicking timing information. Our own dataset includes both types of dynamics from same group of subjects. We develop recurrent neural network (RNN) models and support vector machine (SVM) models to represent user’s biometrics. Keystroke and mouse dynamics can be used as features fed to the models separately for user verification or identification. Feature fusion is applied to improve the accuracy. Our results show the RNN method is better than traditional methods like SVM, and fusion can further improve the performance.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yufeng Zheng, Lixin Yu, Sardar Haque, Ping Zhang, and Adel S. Elmaghraby "Improving cybersecurity through deep learning on keystroke and mouse dynamics", Proc. SPIE 12100, Multimodal Image Exploitation and Learning 2022, 1210005 (27 May 2022); https://doi.org/10.1117/12.2620104
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KEYWORDS
Biometrics

Behavioral biometrics

Distance measurement

Data modeling

Neural networks

Mahalanobis distance

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