In　recent　years,　there　have　been　numerous　cyber　security　issues　that　have　caused　considerable　damage　to　the　society.　The　development　of　efficient　and　reliable　Intrusion　Detection　Systems　(IDSs)　is　an　effective　countermeasure　against　the　growing　cyber　threats.　In　modern　high-bandwidth,　large-scale　network　environments,　traditional　IDSs　suffer　from　a　high　rate　of　missed　and　false　alarms.　Researchers　have　introduced　machine　learning　techniques　into　intrusion　detection　with　good　results.　However,　due　to　the　scarcity　of　attack　data,　such　methods＇　training　sets　are　usually　unbalanced,　affecting　the　analysis　performance.　In　this　paper,　we　survey　and　analyze　the　design　principles　and　shortcomings　of　existing　oversampling　methods.　Based　on　the　findings,　we　take　the　perspective　of　imbalance　and　high　dimensionality　of　datasets　in　the　field　of　intrusion　detection　and　propose　an　oversampling　technique　based　on　Generative　Adversarial　Networks　(GAN)　and　feature　selection.　Specifically,　we　model　the　complex　high-dimensional　distribution　of　attacks　based　on　Gradient　Penalty　Wasserstein　GAN　(WGAN-GP)　to　generate　additional　attack　samples.　We　then　select　a　subset　of　features　representing　the　entire　dataset　based　on　analysis　of　variance,　ultimately　generating　a　rebalanced　low-dimensional　dataset　for　machine　learning　training.　To　evaluate　the　effectiveness　of　our　proposal,　we　conducted　experiments　based　on　the　NSL-KDD,　UNSW-NB15,　and　CICIDS-2017　datasets.　The　experimental　results　show　that　our　method　can　effectively　improve　the　detection　performance　of　machine　learning　models　and　outperform　the　baselines.