Distributed　denial-of-service　attack,　also　known　as　DDoS　attack,　is　one　of　the　most　common　network　attacks　at　present.　With　the　rapid　development　of　computer　and　communication　technology,　the　harm　of　DDoS　attack　is　becoming　more　and　more　serious.　Therefore,　the　research　on　DDoS　attack　detection　becomes　more　important.　Nowadays,　some　related　research　work　has　been　done　and　some　progress　has　been　made.　However,　due　to　the　diversity　of　DDoS　attack　modes　and　the　variable　size　of　attack　traffic,　there　has　not　yet　been　a　detection　method　with　satisfactory　detection　accuracy　at　present.　In　view　of　this,　this　paper　proposes　a　DDoS　attack　detection　method　based　on　machine　learning,　which　includes　two　steps:　feature　extraction　and　model　detection.　In　the　feature　extraction　stage,　the　DDoS　attack　traffic　characteristics　with　a　large　proportion　are　extracted　by　comparing　the　data　packages　classified　according　to　rules.　In　the　model　detection　stage,　the　extracted　features　are　used　as　input　features　of　machine　learning,　and　the　random　forest　algorithm　is　used　to　train　the　attack　detection　model.　The　experimental　results　show　that　the　proposed　DDoS　attack　detection　method　based　on　machine　learning　has　a　good　detection　rate　for　the　current　popular　DDoS　attack.　©　2019　IOP　Publishing　Ltd.　All　rights　reserved.