【摘要】 目前，面向智能物联网场景的用户身份认证方法正蓬勃发展。一些工作利用室内环境中广泛存在的Wi-Fi信号感知用户的行为动作，并提取用户行为动作中蕴含的个体行为的独特性来实现用户身份认证。然而，用户必须在已知域背景(环境、位置、方向)下执行独立的行为动作，系统才能有效地进行身份认证。为突破现有方法的限制，提出了使用Wi-Fi信号感知人体连续行为动作的跨域身份认证系统CroAuth,其能够在用户执行连续行为动作时实现跨环境、位置、方向的用户身份认证。为突破执行独立行为动作的限制，提出了基于动态时间规整的连续行为动作分离算法，在用户多样化的连续行为中分离出特定的行为动作序列，以实现有效的行为信息提取。之后，提出了基于孪生神经网络的跨域身份认证方法，提取域无关的个体行为特征，并进一步利用知识蒸馏方法构建小样本学习的跨域身份认证模型，以实现在不同环境、位置和方向下的用户身份认证。实验结果表明，CroAuth能够在用户执行多样化的连续行为动作时，在跨环境、位置、方向的场景下对用户身份进行认证。更多还原

【Abstract】 Nowadays, Internet of Things(IoT)-based user authentication has been gradually developed.Some works utilize widespread Wi-Fi signals to sense user activities and extract individual uniqueness for user authentication.However, users must perform an independent activity under a known domain(i.e.,environment, location, and orientation),before the system can conduct user authentication.In order to break through the limitation of existing methods, this paper proposes a cross-domain user authentication method based on Wi-Fi signals, CroAuth, to realize user authentication across environments, locations, and orientations when users perform continuous activities.To release the requirement of performing independent activities, this paper proposes a continuous activity separation algorithm based on dynamic time warping, which can separate specific activity sequences from diversified continuous activities.Then, this paper designs a cross-domain user authentication method based on siamese neural network to extract domain-independent features, which can characterize essential behavioral uniqueness of each user under various environments, locations, and orientations.Finally, a knowledge distillation method is utilized to construct a few-shot cross-domain user authentication model.Experimental results show that CroAuth can authenticate users under cross-environment, location, and orientation scenarios when users perform diversified continuous activities.更多还原