With　the　rapid　development　of　network　technology,　the　number　and　variety　of　malware　have　been　increasing,　posing　a　significant　challenge　in　the　field　of　network　security.　However,　existing　single-feature　malware　detection　methods　have　proven　inadequate　in　representing　sample　information　effectively.　Moreover,　multi-feature　detection　approaches　also　face　limitations　in　feature　fusion,　resulting　in　an　inability　to　learn　and　comprehend　the　complex　relationships　within　and　between　features.　These　limitations　ultimately　lead　to　subpar　detection　results.　To　address　these　issues,　a　malware　detection　method　called　MFAGM　was　proposed,　which　focused　on　multimodal　feature　fusion.　By　processing　the　.asm　and　.bytes　files　of　the　dataset,　three　key　features　belonging　to　two　types　(opcode　statistics　sequences,　API　sequences,　and　grey-scale　image　features)　were　successfully　extracted.　This　comprehensive　characterization　of　sample　information　from　multiple　perspectives　aimed　to　improve　detection　accuracy.　In　order　to　enhance　the　fusion　of　these　multimodal　features,　a　feature　fusion　module　called　SA-JGmu　was　designed.　This　module　utilized　the　self-attention　mechanism　to　capture　internal　dependencies　between　features.　It　also　leveraged　the　gating　mechanism　to　enhance　interactivity　among　different　features.　Additionally,　weight-jumping　links　were　introduced　to　further　optimize　the　representational　capabilities　of　the　model.　Experimental　results　on　the　Microsoft　malware　classification　challenge　dataset　demonstrate　that　MFAGM　achieves　higher　accuracy　and　F1　scores　compared　to　other　methods　in　the　task　of　malware　detection.　©　2024　Beijing　Xintong　Media　Co.,　Ltd..　All　rights　reserved.