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Changsheng Wu1 Wenbo Ding1 Ruiyuan Liu1 Zhong Lin Wang1

1, Georgia Institute of Technology, Atlanta, Georgia, United States

Cyber security has become a serious concern as the internet penetrates every corner of our life over the last two decades. The rapidly developing human-machine interfacing calls for an effective and continuous authentication solution. Herein, we developed a two-factor, pressure-enhanced keystroke-dynamics-based security system that is capable of authenticating and even identifying users through their unique typing behavior, with an accuracy up to 98.7%. The system consists of a rationally designed triboelectric keystroke device that converts typing motions into analog electrical signals, and a support vector machine (SVM) algorithm based software platform for user classification. Our active sensing hardware based on triboelectrification is superior to conventional pressure-sensor-based keyboards in terms of system integrity and to previous triboelectric devices in terms of signal quality. Our device has a unique touch-proof feature, i.e. capable of distinguishing inadvertent touch from genuine typing, and thus has an improved signal-to-interference-plus-noise ratio from 2 dB to 10 dB thanks to a specifically designed shield structure. This unconventional silicone-based keystroke device is self-powered, stretchable and water/dust proof, which makes it highly mobile and applicable to versatile working environments. A customized SVM-based software platform was developed and integrated with the triboelectric keystroke device to construct the two-factor authentication/identification system. In the training process, normalized feature vectors are extracted from active typing signals to build user profile models via supervised learning with the help of principle component analysis and SVM. For the implementation of the authentication and identification, a similar process is carried out and the test user profile is cross-referenced with the existing profile database for decision making through a pre-trained LibSVM-based classifier, with a classical binary one for authentication and a multi-class one for identification. For the first time, moreover, the benefits of additional typing features based on signal magnitudes to keystroke dynamics have been quantified using our customized difference score scheme. The promising application of this novel system in the financial and computing industry can push cyber security to the next level, where leaked passwords would possibly be of no concern.
Ref: C. Wu, W. Ding, R. Liu, Z.L. Wang, et al. Keystroke dynamics enabled authentication and identification using triboelectric nanogenerator array. [In submission]

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