Malware　detection　is　indispensable　to　cybersecurity.　However,　with　the　advent　of　new　malware　variants　and　scenarios　with　few　and　imbalanced　samples,　malware　detection　for　various　complex　scenarios　has　been　a　very　challenging　problem.　In　this　article,　we　propose　a　malware　detection　method　based　on　image　analysis　and　generative　adversarial　networks,　named　MadInG,　which　can　improve　the　accuracy　of　malware　detection　for　insufficient　samples,　sample　imbalance,　and　new　variants　scenarios.　Specifically,　we　first　generate　fixed-size　grayscale　images　of　malware　to　reduce　the　workload　of　feature　engineering　or　the　involvement　of　domain　expert　knowledge　on　malware　detection.　Then　we　introduce　auxiliary　classifier　generative　adversarial　networks　into　malware　detection　to　enhance　the　generalization　ability　of　the　detector.　Finally,　we　construct　a　variety　of　malware　scenarios　and　compare　our　proposed　method　with　existing　popular　detection　methods.　Extensive　experimental　results　demonstrate　that　our　method　achieves　high　accuracy　and　well　balance　in　malware　detection　for　different　scenarios,　especially,　the　detection　rate　of　malware　variants　reaches　99.5%.