This　work　concentrates　on　the　adaptive　resilient　dynamic　surface　controller　design　problem　for　uncertain　nonlinear　lower　triangular　stochastic　cyber-physical　systems　(CPSs)　subject　to　unknown　deception　attacks　based　on　a　switching　threshold　event-triggered　mechanism.　The　adverse　effect　of　deception　attacks　on　the　stochastic　CPSs　is　that　the　exact　system　state　variables　become　unavailable.　Furthermore,　it　should　be　emphasized　that　the　coexistence　of　unknown　nonlinearities,　stochastic　perturbations,　and　unknown　sensor　and　actuator　attacks　makes　it　a　very　difficult　and　challenging　event　to　implement　the　control　design.　To　get　the　desired　controller,　radial　basis　function　(RBF)　neural　networks　(NNs)　are　introduced　so　that　the　design　obstacle　caused　from　the　unknown　nonlinearities　can　be　easily　solved.　On　this　basis,　in　order　to　save　resources　and　effectively　transmit,　the　event-triggered　control　scheme　based　on　a　switching　threshold　strategy　is　further　considered.　In　the　backstepping　design　process,　the　dynamic　surface　control　(DSC)　technique　is　presented　to　deal　with　the　issue　of　＂explosion　of　complexity.＂　By　skillfully　designing　a　new　coordinate　transformation　and　the　attack　compensators,　the　problem　of　unknown　deception　attacks　is　successfully　handled.　Under　our　proposed　control　scheme,　all　the　closed-loop　signals　are　bounded　in　probability　and　the　stabilization　errors　converge　to　an　adjustable　neighborhood　of　the　origin　in　probability.　Finally,　the　simulation　results　on　the　double　chemical　reactor　show　the　validity　of　the　proposed　design　scheme.