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.

Key words: Wi-Fi sensing, User authentication, Continuous activities, Cross-domain scenario, Siamese neural network, Few-short learning