The　intelligence-embedded　devices　also　called　the　Internet　of　Things,　have　heterogeneous　characteristics　and　generate　enormous　data.　To　oblige　the　expansion　of　smart　devices　in　the　age　of　data,　software-defined　networking　(SDN)　has　emerged　as　a　promising　cost-effective,　scalable,　versatile　solution　for　IoT　services.　However,　the　proliferation　and　pervasiveness　of　IoT　devices　also　bring　some　serious　security　concerns　of　cyber　threats　and　attacks.　This　work　presents　a　real-time　intrusion　detection　and　mitigation　solution　for　SDN-enabled　IoT　infrastructure　to　provide　autonomous　security　in　high-traffic　IoT　networks　in　the　5G　and　beyond　future.　The　suggested　approach　used　an　ensemble　of　Convolution　Long　short-term　memory　+　Bidirectional　Long　short-term　memory　(ConvLSTM2D+BLSTM)　at　the　SDN　network　layer　to　automate　flow　feature　extraction　and　classification.　For　evolving　distributed　denial　of　service　(DDoS)　attacks,　the　testing　is　conducted　on　the　CICIDS2017　dataset.　The　experimental　results　showed　that　the　proposed　security　approach　could　detect　and　mitigate　threats　in　real-time　with　high　accuracy　of　99.69%　rate,　99.93%　precision,　and　99.65%　F1-score　by　performing　10-fold　cross-validation.