As　a　core　component　of　the　network,　web　applications　have　become　one　of　the　preferred　targets　for　attackers　because　the　static　configuration　of　web　applications　simplifies　the　exploitation　of　vulnerabilities　by　attackers.　Although　the　moving　target　defense　(MTD)　has　been　proposed　to　increase　the　attack　difficulty　for　the　attackers,　there　is　no　solo　approach　can　cope　with　different　attacks;　in　addition,　it　is　impossible　to　implement　all　these　approaches　simultaneously　due　to　the　resource　limitation.　Thus,　the　selection　of　an　optimal　defense　strategy　based　on　MTD　has　become　the　focus　of　research.　In　general,　the　confrontation　of　two　players　in　the　security　domain　is　viewed　as　a　stochastic　game,　and　the　reward　matrices　are　known　to　both　players.　However,　in　a　real　security　confrontation,　this　scenario　represents　an　incomplete　information　game.　Each　player　can　only　observe　the　actions　performed　by　the　opponent,　and　the　observed　actions　are　not　completely　accurate.　To　accurately　describe　the　attacker＇s　reward　function　to　reach　the　Nash　equilibrium,　this　work　simulated　and　updated　the　strategy　selection　distribution　of　the　attacker　by　observing　and　investigating　the　strategy　selection　history　of　the　attacker.　Next,　the　possible　rewards　of　the　attacker　in　each　confrontation　via　the　observation　matrix　were　corrected.　On　this　basis,　the　Nash-Q　learning　algorithm　with　reward　quantification　was　proposed　to　select　the　optimal　strategy.　Moreover,　the　performances　of　the　Minimax-Q　learning　algorithm　and　Naive-Q　learning　algorithm　were　compared　and　analyzed　in　the　MTD　environment.　Finally,　the　experimental　results　showed　that　the　strategy　selection　algorithm　can　enable　defenders　to　select　a　more　reasonable　defensive　strategy　and　achieve　the　maximum　possible　reward.