Behavioral Biometrics for Continuous Authentication in the Internet-of-Things Era: An Artificial Intelligence Perspective
Behavioral Biometrics for Continuous Authentication in the Internet-of-Things Era: An Artificial Intelligence Perspective
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DOI：
10.1109/jiot.2020.3004077
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发表时间：
2020-09-01
影响因子：
10.6
通讯作者：
Yu, Zhiwen
中科院分区：
文献类型：
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作者：
Liang, Yunji;Samtani, Sagar;Yu, Zhiwen
在物联网（IoT）时代，用户认证对于确保连接设备的安全性以及被动服务的定制至关重要。然而，传统的基于知识和生理生物特征的认证系统（例如密码、人脸识别和指纹识别）容易受到肩窥攻击、污迹攻击和热攻击。物联网设备（包括智能手机、可穿戴设备、机器人和自动驾驶车辆）强大的感知能力使得基于行为生物特征的连续认证（CA）成为可能。人工智能（AI）方法在筛选大量异构生物特征数据以提供前所未有的用户认证和用户识别能力方面具有巨大潜力。在这篇综述文章中，我们概述了物联网应用中连续认证的性质，强调了关键的行为信号，并从人工智能的角度总结了现有的解决方案。基于我们系统而全面的分析，我们讨论了挑战以及有前景的未来方向，以指导下一代基于人工智能的连续认证研究。
In the Internet-of-Things (IoT) era, user authentication is essential to ensure the security of connected devices and the customization of passive services. However, conventional knowledge-based and physiological biometric-based authentication systems (e.g., password, face recognition, and fingerprints) are susceptible to shoulder surfing attacks, smudge attacks, and heat attacks. The powerful sensing capabilities of IoT devices, including smartphones, wearables, robots, and autonomous vehicles enable continuous authentication (CA) based on behavioral biometrics. The artificial intelligence (AI) approaches hold significant promise in sifting through large volumes of heterogeneous biometrics data to offer unprecedented user authentication and user identification capabilities. In this survey article, we outline the nature of CA in IoT applications, highlight the key behavioral signals, and summarize the extant solutions from an AI perspective. Based on our systematic and comprehensive analysis, we discuss the challenges and promising future directions to guide the next generation of AI-based CA research.