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27 May 2022 Improving cybersecurity through deep learning on keystroke and mouse dynamics
Yufeng Zheng, Lixin Yu, Sardar Haque, Ping Zhang, Adel S. Elmaghraby
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Proceedings Volume 12100, Multimodal Image Exploitation and Learning 2022; 1210005 (2022) https://doi.org/10.1117/12.2620104
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States
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.
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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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