Smart　home　systems　are　both　technologically　and　economically　advancing　rapidly.　As　people　become　gradually　inalienable　to　smart　home　infrastructures,　their　security　conditions　are　getting　more　and　more　closely　tied　to　everyone’s　privacy　and　safety.　In　this　paper,　we　consider　smart　apps,　either　malicious　ones　with　evil　intentions　or　benign　ones　with　logic　errors,　that　can　cause　property　loss　or　even　physical　sufferings　to　the　user　when　being　executed　in　a　smart　home　environment　and　interacting　with　human　activities　and　environmental　changes.　Unfortunately,　current　preventive　measures　rely　on　permissionbased　access　control,　failing　to　provide　ideal　protections　against　such　threats　due　to　the　nature　of　their　rigid　designs.　In　this　paper,　we　propose　CommandFence,　a　novel　digital-twin-based　security　framework　that　adopts　a　fundamentally　new　concept　of　protecting　the　smart　home　system　by　letting　any　sequence　of　app　commands　to　be　executed　in　a　virtual　smart　home　system,　in　which　a　deep-q　network　(DQN)　is　used　to　predict　if　the　sequence　could　lead　to　a　risky　consequence.　CommandFence　is　composed　of　an　Interposition　Layer　to　interpose　app　commands　and　an　Emulation　Layer　to　figure　out　whether　they　can　cause　any　risky　smart　home　state　if　correlating　with　possible　human　activities　and　environmental　changes.　We　fully　implemented　our　CommandFence　implementation　and　tested　against　553　official　SmartApps　on　the　Samsung　SmartThings　platform　and　successfully　identified　34　potentially　dangerous　ones,　with　31　of　them　reported　to　be　problematic　the　first　time　to　our　best　knowledge.　Moreover,　We　tested　our　CommandFence　on　the　10　malicious　SmartApps　created　by　[1]　and　successfully　identified　7　of　them　as　risky,　with　the　missed　ones　actually　only　causing　smartphone　information　leak　(not　harmful　to　the　smart　home　system).　We　also　tested　CommandFence　against　the　17　benign　SmartApps　with　logic　errors　developed　by　[2]　and　achieved　a　100　CommandFence　incurs　a　neglectable　overhead　of　0.1675　seconds.　IEEE