The authentication field is evolving towards mechanisms able to keep users continuously authenticated without the necessity of remembering or possessing authentication credentials. While existing continuous authentication systems have demonstrated their suitability for single-device scenarios, the Internet of Things and next generation of mobile networks (5G) are enabling novel multi-device scenarios -- such as Smart Offices -- where continuous authentication is still an open challenge. The paper at hand, proposes an AI-based, privacy-preserving and multi-device continuous authentication architecture called AuthCODE. A realistic Smart Office scenario with several users, interacting with their mobile devices and personal computer, has been used to create a set of single- and multi-device behavioural datasets and validate AuthCODE. A pool of experiments with machine and deep learning classifiers measured the impact of time in authentication accuracy and improved the results of single-device approaches by considering multi-device behaviour profiles. The f1-score average reached for XGBoost on multi-device profiles based on 1-minute windows was 99.33%, while the best performance achieved for single devices was lower than 97.39%. The inclusion of temporal information in the form of vector sequences classified by a Long-Short Term Memory Network, allowed the identification of additional complex behaviour patterns associated to each user, resulting in an average f1-score of 99.02% on identification of long-term behaviours.

中文翻译：

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AuthCODE：一种基于机器学习和深度学习的隐私保护和多设备持续认证架构

身份验证领域正在朝着能够保持用户持续身份验证的机制发展，而无需记住或拥有身份验证凭据。虽然现有的连续身份验证系统已经证明它们适用于单设备场景，但物联网和下一代移动网络 (5G) 正在支持新的多设备场景——例如智能办公室——其中连续身份验证仍然是开放的挑战。手头的论文提出了一种名为 AuthCODE 的基于 AI 的、保护隐私的多设备连续身份验证架构。多个用户与其移动设备和个人计算机交互的真实智能办公场景已被用于创建一组单设备和多设备行为数据集并验证 AuthCODE。机器和深度学习分类器的一系列实验测量了时间对身份验证准确性的影响，并通过考虑多设备行为配置文件改进了单设备方法的结果。XGBoost 在基于 1 分钟窗口的多设备配置文件上达到的 f1-score 平均值为 99.33%，而在单台设备上实现的最佳性能低于 97.39%。以由长期短期记忆网络分类的向量序列形式包含时间信息，允许识别与每个用户相关联的额外复杂行为模式，从而导致长期识别的平均 f1-score 为 99.02%行为。