AI 中文总结
该研究针对跨设备击键认证的分布漂移问题，提出基于归纳迁移学习的系统，结合适配数据与少量目标设备训练数据训练分类器，在BBMAS数据集上实现14.2%的等错误率，性能优于现有方法。
AI 中文摘要
击键动力学（即打字模式）可作为行为生物特征模态用于用户认证，应用场景包括欺诈预防。该模态在单设备认证中表现良好，但应用于跨设备场景时更具挑战性：在一个设备（如手机）上学习到的动力学，因打字模式变化可能导致分布漂移，无法直接应用于另一不同外形规格的设备（如平板电脑）的认证。为解决此问题，本文提出一种基于归纳迁移学习的跨设备用户认证系统，将在一个设备上学到的击键动力学适配至另一设备，再将适配后的数据与该设备有限的训练数据结合，用于鲁棒训练二分类器；此外，采用扩展的击键特征集以更好捕捉具有区分性的动力学。在BBMAS数据集上的实验表明，该系统在跨设备场景下的等错误率为14.2%，优于现有最先进方法。
英文摘要
Keystroke dynamics (typing patterns) can be used as a behavioural biometric modality for user authentication, with applications such as fraud prevention. While the modality has been shown to work well for single device authentication, its application to cross-device scenarios is more challenging. Dynamics learned on one device (eg., phone) may not be directly applicable to authentication on a secondary device with a different form factor (eg., tablet) due to changes in typing patterns that can lead to distribution drifts. To address this, we propose a cross-device user authentication system based on inductive transfer learning, where keystroke dynamics learned on one device are adapted to a secondary device. The adapted data is then combined with necessarily limited training data for the secondary device, which is used to robustly train a binary classifier. Furthermore, an extended set of keystroke features is used to better capture discriminative dynamics. Experiments on the BBMAS dataset show that proposed system achieves an equal error rate of 14.2% for the cross-device scenario, surpassing previous methods.
