As　the　scale　of　the　networks　continually　expands,　the　detection　of　distributed　denial　of　service　(DDoS)　attacks　has　become　increasingly　vital.　We　propose　an　intelligent　detection　model　named　IGED　by　using　improved　generalized　entropy　and　deep　neural　network　(DNN).　The　initial　detection　is　based　on　improved　generalized　entropy　to　filter　out　as　much　normal　traffic　as　possible,　thereby　reducing　data　volume.　Then　the　fine　detection　is　based　on　DNN　to　perform　precise　DDoS　detection　on　the　filtered　suspicious　traffic,　enhancing　the　neural　network’s　generalization　capabilities.　Experimental　results　show　that　the　proposed　method　can　efficiently　distinguish　normal　traffic　from　DDoS　traffic.　Compared　with　the　benchmark　methods,　our　method　reaches　99.9%　on　low-rate　DDoS　(LDDoS),　flooded　DDoS　and　CICDDoS2019　datasets　in　terms　of　both　accuracy　and　efficiency　in　identifying　attack　flows　while　reducing　the　time　by　17%,　31%　and　8%.　©　2024　Tech　Science　Press.　All　rights　reserved.