@inproceedings{3006d0993041498dafbb36a4b6ae90da,
title = "Detection of Behavior Aging from Keystroke Dynamics",
abstract = "Keystroke dynamics-based authentication (KDA) is one of human behavioral-based authentication methods based on the unique typing rhythm of an individual. Nevertheless, the typing characteristics gradually change over time. Various solutions have been suggested to remedy the concept drift problem, including multimodal and unimodal adaptive methods. However, these solutions don't consider that temporal concept drift has a negative impact on performance and update frequency increases computation cost. The paper proposes weighted EDDM to detect concept drift and capture permanent concept drift (behavioral natural aging). Experimental results show that our method can accurately capture behavioral natural aging and filter temporal concept drift. Our proposed method has better performance and less computation.",
keywords = "aging detection, behavioral aging, concept drift, keystroke dynamics, weighted EDDM",
author = "Yafang Yang and Bin Guo and Yunji Liang and Zhiwen Yu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021 ; Conference date: 14-12-2021 Through 16-12-2021",
year = "2021",
doi = "10.1109/ICPADS53394.2021.00078",
language = "英语",
series = "Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS",
publisher = "IEEE Computer Society",
pages = "583--590",
booktitle = "Proceedings - 2021 IEEE 27th International Conference on Parallel and Distributed Systems, ICPADS 2021",
}