To　solve　the　deficiency　of　the　malicious　code　detector’s　ability　to　detect　adversarial　input,　an　adversarial　training　driven　malicious　code　detection　enhancement　method　was　proposed.　Firstly,　the　applications　were　preprocessed　by　a　decompiler　tool　to　extract　API　call　features　and　map　them　into　binary　feature　vectors.　Secondly,　the　Wasserstein　generative　adversarial　network　was　introduced　to　build　a　benign　sample　library　to　provide　a　richer　combination　of　perturbations　for　malicious　sample　evasion　detectors.　Then,　a　perturbation　reduction　algorithm　based　on　logarithmic　backtracking　was　proposed.　The　benign　samples　were　added　to　the　malicious　code　in　the　form　of　perturbations,　and　the　added　benign　perturbations　were　culled　dichotomously　to　reduce　the　number　of　perturbations　with　fewer　queries.　Finally,　the　adversarial　malicious　code　samples　were　marked　as　malicious　and　the　detector　was　retrained　to　improve　its　accuracy　and　robustness　of　the　detector.　The　experimental　results　show　that　the　generated　malicious　code　adversarial　samples　can　evade　the　detector　well.　Additionally,　the　adversarial　training　increases　the　target　detector’s　accuracy　and　robustness.　©　2022　Editorial　Board　of　Journal　on　Communications.　All　rights　reserved.