In　mobile　fog　computing　(MFC),　data　processing　and　applications　are　concentrated　in　devices　at　the　network＇s　edge.　This　means　that　data　can　be　processed　in　local　intelligent　devices　without　being　sent　to　the　cloud　for　execution,　bringing　great　convenience　to　end-users.　Fog　computing　is　a　semi-virtualised　service　computing　architecture　model　between　cloud　computing　and　personal　computing.　However,　its　virtualized　environment　is　vulnerable　to　advanced　persistent　threats　(APT).　In　this　paper,　we　propose　an　APT　defense　scheme　based　on　MFC＇s　Double　Q-learning　(DQL)　algorithm.　First,　we　apply　prospect　theory　(PT)　to　build　a　static　subjective　game　model　between　APT　attackers　and　legitimate　users.　In　addition,　a　dynamic　game　scheme　based　on　DQL　is　proposed　against　APT　attacks.　Finally,　we　compare　our　proposed　method　with　the　existing　methods,　i.e.,　the　Q-learning　algorithm,　Sarsa　algorithm　and　Greedy　algorithm.　The　experimental　results　show　that　the　proposed　method　can　effectively　suppress　the　attack　motivation　of　APT　attackers,　improve　the　utility　of　legitimate　users,　and　protect　the　security　of　the　fog　computing　environment.