Behavior-based　Wi-Fi　user　authentication　has　gained　popularity　in　user-centered　smart　systems.　However,　its　wide　adoption　has　been　hindered　by　certain　critical　issues,　including　significant　performance　degradation　when　the　environment　changes,　the　inability　to　handle　unknown　activities,　and　weak　security　due　to　basing　authentication　on　the　recognition　of　a　single,　one-off　activity.　In　this　paper,　we　propose　Wi-Dist,　which　authenticates　a　user　using　a　behavior　password,　i.e.　a　pre-chosen　sequence　of　activities.　Wi-Dist　addressed　the　previously　mentioned　technical　challenges　through　a　cross-layer　joint　optimization　framework.　In　particular,　we　address　environment　dependency　by　incorporating　adversarial　learning　and　optimizing　both　the　signal　layer　and　the　domain　adaptation　layer.　This　enhances　the　performance　of　the　learned　model　across　various　environments.　To　effectively　handle　unknown　behaviors,　we　utilize　an　adversarial　learning-based　network.　This　network　establishes　a　pseudo-decision　boundary　between　samples　from　known　and　unknown　sources,　ensuring　robust　authentication.　Additionally,　for　authentication　using　continuous　activities,　we　employ　double-sliding　windows　activity　monitoring.　This　approach,　coupled　with　activity　state　correction,　partitions　activities　for　accurate　recognition.　We　also　conducted　extensive　experiments　in　indoor　environments　to　demonstrate　that　Wi-Dist　is　effective　and　robust.