Piezoelectric and Machine Learning Based Keystroke Dynamics for Highly Secure User Authentication
Chenyu Tang, Ziang Cui, Meng Chu, Yujiao Lu, Fuqiang Zhou, Shuo Gao
Abstract
Cyber security is of significance in today’s e-commerce applications. In this article, we present a piezoelectric touch sensing supported keystroke dynamics based identity authentication technique, for providing a secure access manner to smartphones. Here, the polyvinylidene fluoride (PVDF) based piezoelectric touch panel can learn detailed force touch habits of users. With a support vector machine (SVM) algorithm, our proposed frequency domain features experimentally demonstrate a better authentication accuracy of 98.3%, compared to the traditional time domain features. The work showcases a feasible method of combining functional materials and artificial intelligence (AI) for satisfying highly secure requirements.