The　detection　and　removal　of　malicious　social　bots　in　social　networks　has　become　an　area　of　interest　in　industry　and　academia.　The　widely　used　bot　detection　method　based　on　machine　learning　leads　to　an　imbalance　in　the　number　of　samples　in　different　categories.　Classifier　bias　leads　to　a　low　detection　rate　of　minority　samples.　Therefore,　we　propose　an　improved　conditional　generative　adversarial　network　(improved　CGAN)　to　extend　imbalanced　data　sets　before　applying　training　classifiers　to　improve　the　detection　accuracy　of　social　bots.　To　generate　an　auxiliary　condition,　we　propose　a　modified　clustering　algorithm,　namely,　the　Gaussian　kernel　density　peak　clustering　algorithm　(GKDPCA),　which　avoids　the　generation　of　data-augmentation　noise　and　eliminates　imbalances　between　and　within　social　bot　class　distributions.　Furthermore,　we　improve　the　CGAN　convergence　judgment　condition　by　introducing　the　Wasserstein　distance　with　a　gradient　penalty,　which　addresses　the　model　collapse　and　gradient　disappearance　in　the　traditional　CGAN.　Three　common　oversampling　algorithms　are　compared　in　experiments.　The　effects　of　the　imbalance　degree　and　the　expansion　ratio　of　the　original　data　on　oversampling　are　studied,　and　the　improved　CGAN　performs　better　than　the　others.　Experimental　results　comparing　with　three　common　oversampling　algorithms　show　that　the　improved　CGAN　achieves　the　higher　evaluation　scores　in　terms　of　F1-score,　G-mean　and　AUC.