With　the　development　of　society,　network　security　has　received　more　and　more　attention.　Malicious　code　has　also　grown,　causing　network　security　vulnerabilities　and　increasing　threats　to　internet　security.　Therefore,　the　detection　of　malicious　code　becomes　very　important.　However,　there　are　some　problems　in　the　current　research　on　malicious　code　detection,　for　example,　tedious　feature　extraction　and　unbalanced　data,　which　is　far　from　the　effect　people　want　to　achieve.　To　address　these　problems,　in　this　paper,　we　propose　a　novel　malicious　code　detection　and　fine-grained　classification　model　by　using　convolutional　neural　networks　and　swarm　intelligence　algorithms.　We　converted　the　binary　executable　files　of　malicious　codes　into　greyscale　images　and　then　used　convolution　neural　networks　to　detect　and　classify　malicious　codes.　In　addition,　we　employed　swarm　intelligence　algorithms　to　achieve　fine-grained　classification　on　unbalanced　data　in　different　malicious　code　families.　We　conducted　a　series　of　experiments　on　the　real　malware　dataset　from　Vision　Research　Lab.　The　experimental　results　demonstrated　that　the　proposed　solution　is　effective　for　fine-grained　classification　of　malicious　codes.　©　2020　Inderscience　Enterprises　Ltd..　All　rights　reserved.