As　an　essential　piece　of　infrastructure　supporting　cyberspace　security　technology　verification,　network　weapons　and　equipment　testing,　attack　defense　confrontation　drills,　and　network　risk　assessment,　Cyber　Range　is　exceptionally　vulnerable　to　distributed　denial　of　service　(DDoS)　attacks　from　three　malicious　parties.　Moreover,　some　attackers　try　to　fool　the　classification/prediction　mechanism　by　crafting　the　input　data　to　create　adversarial　attacks,　which　is　hard　to　defend　for　ML-based　Network　Intrusion　Detection　Systems　(NIDSs).　This　paper　proposes　an　adversarial　DBN-LSTM　method　for　detecting　and　defending　against　DDoS　attacks　in　SDN　environments,　which　applies　generative　adversarial　networks　(GAN)　as　well　as　deep　belief　networks　and　long　short-term　memory　(DBN-LSTM)　to　make　the　system　less　sensitive　to　adversarial　attacks　and　faster　feature　extraction.　We　conducted　the　experiments　using　the　public　dataset　CICDDoS　2019.　The　experimental　results　demonstrated　that　our　method　efficiently　detected　up-to-date　common　types　of　DDoS　attacks　compared　to　other　approaches.　©　2023　by　the　authors.