Device adaptation free-KDA based on multi-teacher knowledge distillation

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-08-12 DOI:10.1007/s12652-024-04836-5
Yafang Yang, Bin Guo, Yunji Liang, Kaixing Zhao, Zhiwen Yu
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Abstract

The keyboard, a major mean of interaction between human and internet devices, should beset right for good performance during authentication task. To guarantee that one legitimate user can interleave or simultaneously interact with two or more devices with protecting user privacy, it is essential to build device adaptation free-text keystroke dynamics authentication (free-KDA) model based on multi-teacher knowledge distillation methods. Some multi-teacher knowledge distillation methods have shown effective in C-way classification task. However, it is unreasonable for free-KDA model, since free-KDA model is one-class classification task. Instead of using soft-label to capture useful knowledge of source for target device, we propose a device adaptation free-KDA model. When one user builds the authentication model for target device with limited training samples, we propose a novel optimization objective by decreasing the distance discrepancy in Euclidean distance and cosine similarity between source and target device. And then, we adopt an adaptive confidence gate strategy to solve different correlation for each user between different source devices and target device. It is verified on two keystroke datasets with different types of keyboards, and compared its performance with the existing dominant multi-teacher knowledge distillation methods. Extensive experimental results demonstrate that AUC of target device reaches up to 95.17%, which is 15.28% superior to state-of-the-art multi-teacher knowledge distillation methods.

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基于多教师知识提炼的设备自适应自由 KDA
键盘是人与互联网设备交互的主要工具,要想在身份验证任务中取得良好的性能,就必须正确使用键盘。为了保证一个合法用户能与两台或多台设备交错或同时交互,同时保护用户隐私,必须基于多教师知识提炼方法建立设备自适应自由文本按键动态验证(free-KDA)模型。一些多教师知识提炼方法在 C 路分类任务中表现出了良好的效果。然而,这对于自由 KDA 模型来说是不合理的,因为自由 KDA 模型是单类分类任务。我们提出了一种设备适配自由 KDA 模型,而不是使用软标签来捕获源设备对目标设备的有用知识。当用户利用有限的训练样本为目标设备建立认证模型时,我们提出了一个新的优化目标,即减小源设备和目标设备之间的欧氏距离和余弦相似度的距离差异。然后,我们采用自适应置信门策略来解决每个用户在不同源设备和目标设备之间的不同相关性问题。该方法在两个不同类型键盘的按键数据集上进行了验证,并与现有的主流多教师知识提炼方法进行了性能比较。大量实验结果表明,目标设备的 AUC 高达 95.17%,比最先进的多教师知识提炼方法高出 15.28%。
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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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