.png){kind=icon}
返回
Biometric Identification Based on Keystroke Dynamics
.png){kind=icon}
OA
AI
DOI：10.3390/s22093158.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The purpose of the paper is to study how changes in neural network architecture and its hyperparameters affect the results of biometric identification based on keystroke dynamics. The publicly available dataset of keystrokes was used, and the models with different parameters were trained using this data. Various neural network layers-convolutional, recurrent, and dense-in different configurations were employed together with pooling and dropout layers. The results were compared with the state-of-the-art model using the same dataset. The results varied, with the best-achieved accuracy equal to 82% for the identification (1 of 20) task.
Keyword:
neural network
biometric identification
keystroke dynamics
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Characterization of MADS-domain transcription factor complexes in Arabidopsis flower development.png)
IF0
PREAI
分享收藏
分享收藏分享收藏Sodium–glucose cotransporter-2 inhibitor combination therapy to optimize glycemic control and tolerability in patients with type 2 diabetes: focus on dapagliflozin–metformin钠-葡萄糖协同转运蛋白-2抑制剂联合疗法以优化2型糖尿病患者的血糖控制和耐受性：聚焦达格列净-二甲双胍OAAI
OAAI分享收藏
查看更多内容