In　the　present　era,　the　menace　of　malicious　software　is　growing　continuously,　posing　a　significant　challenge　in　the　realm　of　cybersecurity.　Currently,　traditional　machine　learning　algorithms　have　been　widely　applied　to　the　analysis　of　malicious　software　and　are　considered　effective　methods.　However,　these　approaches　often　require　extensive　feature　engineering,　feature　learning,　and　feature　representation,　which　can　be　time-consuming　and　resource-intensive.　In　this　paper,　a　method　for　the　identification　of　malicious　software,　named　＇AntiMal,＇is　proposed.　This　method　utilizes　the　Swin　Transformer　as　the　backbone　network　and　combines　it　with　image　features　for　static　analysis.　It　leverages　the　dataset　provided　by　Microsoft　for　the　Kaggle　Microsoft　Malware　Classification　Challenge.　Initially,　it　converts　the　binary　data　of　malicious　code　into　grayscale　images,　treating　every　8　bits　as　a　pixel.　Subsequently,　it　employs　PyTorch　to　build　and　train　the　Swin　Transformer,　classifying　the　images　in　the　validation　set.　The　results　demonstrate　that　this　approach　excels　in　the　problem　of　malicious　software　classification,　achieving　an　impressive　accuracy　of　up　to　95%,　showcasing　remarkable　precision　and　generalization　capabilities.　Furthermore,　it　exhibits　significant　advantages　when　dealing　with　large　datasets,　requiring　fewer　computational　resources,　thus　offering　exceptional　computational　efficiency.　©　2023　ACM.