To　explore　the　advantages　of　adversarial　learning　and　deep　learning,　we　propose　a　novel　network　intrusion　detection　model　called　SAVAER-DNN,　which　can　not　only　detect　known　and　unknown　attacks　but　also　improve　the　detection　rate　of　low-frequent　attacks.　SAVAER　is　a　supervised　variational　auto-encoder　with　regularization,　which　uses　WGAN-GP　instead　of　the　vanilla　GAN　to　learn　the　latent　distribution　of　the　original　data.　SAVAER＇s　decoder　is　used　to　synthesize　samples　of　low-frequent　and　unknown　attacks,　thereby　increasing　the　diversity　of　training　samples　and　balancing　the　training　data　set.　SAVAER＇s　encoder　is　used　to　initialize　the　weights　of　the　hidden　layers　of　the　DNN　and　explore　high-level　feature　representations　of　the　original　samples.　The　benchmark　NSL-KDD　(KDDTest+),　NSL-KDD　(KDDTest-21)　and　UNSW-NB15　datasets　are　used　to　evaluate　the　performance　of　the　proposed　model.　The　experimental　results　show　that　the　proposed　SAVAER-DNN　is　more　suitable　for　data　augmentation　than　the　other　three　well-known　data　oversampling　methods.　Moreover,　the　proposed　SAVAER-DNN　outperforms　eight　well-known　classification　models　in　detection　performance　and　is　more　effective　in　detecting　low-frequent　and　unknown　attacks.　Furthermore,　compared　with　other　state-of-the-art　intrusion　detection　models　reported　in　the　IDS　literature,　the　proposed　SAVAER-DNN　offers　better　performance　in　terms　of　overall　accuracy,　detection　rate,　F1　score,　and　false　positive　rate.