材料科学
密码
生物识别
击键记录
摩擦电效应
认证(法律)
黑客
人机交互
嵌入式系统
计算机安全
计算机科学
复合材料
作者
Wenqiu Liu,Sen Zeng,Qi Wang,Weiqi Cao,Kecen Li,Xiangbao Zeng,Lixia Guo,Yu Hua
出处
期刊:Nano Energy
[Elsevier BV]
日期:2024-02-16
卷期号:123: 109399-109399
被引量:7
标识
DOI:10.1016/j.nanoen.2024.109399
摘要
The increasing prevalence of the internet in our daily lives has made cybersecurity and banking security crucial concerns. Traditional methods such as passwords, fingerprints, and face recognition are becoming replicable and susceptible to hacking. To address this issue, we developed innovative biometric keystroke dynamics for personal authentication utilizing friction electric sensors. A single-electrode triboelectric nanogenerator utilizes the skin as a positive friction layer, allowing direct contact with the sensor. This arrangement enables the detection of subtle mechanical changes during pressing. To mitigate the impact of sweat and organic pollutants, a bionic rose petal friction layer is incorporated, ensuring consistent output and long-term effectiveness. The sensor efficiently converts mechanical keystroke actions into electrical signals for individuals and transmits them to an artificial neural network-based AI system. By utilizing a self-powered sweat- and dirt-proof biometric authentication system, along with the LSTM neural network algorithm, we have achieved an impressive accuracy rate of 97%. This system provides a promising security layer against password cracking and user privacy vulnerabilities.
科研通智能强力驱动
Strongly Powered by AbleSci AI