WiONE: One-Shot Learning for Environment-Robust Device-Free User Authentication via Commodity Wi-Fi in Man-Machine System
WiONE: One-Shot Learning for Environment-Robust Device-Free User Authentication via Commodity Wi-Fi in Man-Machine System
复制标题
WiONE：通过人机系统中的商用 Wi-Fi 一次性学习环境稳健的无设备用户身份验证
DOI：
10.1109/tcss.2021.3056654
复制
发表时间：
2021-06-01
影响因子：
5
通讯作者：
Ren, Fuji
中科院分区：
文献类型：
--
作者：
Gu, Yu;Yan, Huan;Ren, Fuji
用户身份验证是保护人机系统免受恶意欺骗的第一步，也是最关键的一步。然而，安全和隐私就像一枚硬币的两面，很难同时看到两者，尤其是当前主流的基于凭证和生物识别的方法。为此，我们提出 WiONE，这是一种安全且保护隐私的用户身份验证系统，利用无处不在的 Wi-Fi 基础设施，通过探索“你的行为方式”而不是“你是谁”。关键思想是将深度学习应用于 Wi-Fi 信道状态信息 (CSI) 捕获的用户物理行为，以识别合法用户，同时拒绝欺骗者。 WiONE 的设计面临两个挑战，即如何捕获微妙的行为，例如 CSI 上的按键，以及如何减轻深度学习所需的繁重的特定环境训练。对于前者，我们设计了一种基于莱斯衰落的行为增强模型，通过抑制信道响应上与行为无关的信息来突出行为引起的信息。对于后者，我们开发了一种针对原型网络量身定制的行为表征方法，以方便提取与域无关的行为特征，并能够在新环境中一次性识别新用户。在多个现实环境中进行了大量实验，结果表明 WiONE 在身份验证性能方面优于其最先进的竞争对手，并且训练量要少得多。
User authentication is the first and most critical step in protecting a man-machine system from a malicious spoofer. However, security and privacy are just like the two sides of one coin, hard to see both at the same time, especially by the current mainstream credential- and biometric-based approaches. To this end, we propose WiONE, a safe and privacy-preserving user authentication system leveraging the ubiquitous Wi-Fi infrastructure by exploring "how you behave" rather than "who you are". The key idea is to apply deep learning to user physical behavior captured by Wi-Fi channel state information (CSI) to identify legitimate users while rejecting spoofers. The design of WiONE faces two challenges, namely, how to capture the subtle behavior, such as a keystroke on CSI, and how to mitigate the heavy environment-specific training required by deep learning. For the former, we design a behavior enhancement model based on the Rician fading to highlight the behavior-induced information by suppressing the behavior-unrelated information on channel response. For the latter, we develop a behavior characterization method tailored for the prototypical networks to facilitate the extraction of the domain-independent behavioral features and enable one-shot recognition of a new user in a new environment. Numerous experiments are conducted in several real-world environments, and the results show that WiONE outperforms its state-of-the-art rivals in authentication performance with much less training effort.