arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.07877cs.CR
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AuthCODE:一种基于机器和深度学习的隐私保护和多设备连续认证架构

AuthCODE: A Privacy-preserving and Multi-device Continuous Authentication Architecture based on Machine and Deep Learning

Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Lorenzo Fernández Maimó, Gregorio Martínez Pérez

摘要:

认证领域正在朝着能够保持用户持续认证而无需记住或拥有认证凭证的机制发展。虽然现有的连续身份验证系统已证明其适用于单设备场景,但物联网和下一代移动网络(5G)正在实现新型的多设备场景-例如智能办公室-其中连续身份验证仍然是一个开放的挑战。本文提出了一种基于人工智能的、保护隐私的、多设备连续身份验证体系结构,称为AuthCODE。一个现实的智能办公室场景,多个用户,与他们的移动设备和个人电脑进行交互,已被用来创建一组单设备和多设备的行为数据集和验证AuthCODE。使用机器和深度学习分类器进行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来改进单设备方法的结果。XGBoost在基于1分钟窗口的多设备配置文件上达到的f1分数平均值为99.33%,而单设备的最佳性能低于97.39%。包含的时间信息的形式的矢量序列分类的长短期记忆网络,允许识别额外的复杂的行为模式与每个用户,导致在一个平均的f1-得分99.02%的长期行为的识别。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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