Anomaly　network　detection　is　a　very　important　way　to　analyze　and　detect　malicious　behavior　in　network.　How　to　effectively　detect　anomaly　network　flow　under　the　pressure　of　big　data　is　a　very　important　area,　which　has　attracted　more　and　more　researchers＇　attention.　In　this　paper,　we　propose　a　new　model　based　on　big　data　analysis,　which　can　avoid　the　influence　brought　by　adjustment　of　network　traffic　distribution,　increase　detection　accuracy　and　reduce　the　false　negative　rate.　Simulation　results　reveal　that,　compared　with　k-means,　decision　tree　and　random　forest　algorithms,　the　proposed　model　has　a　much　better　performance,　which　can　achieve　a　detection　rate　of　95.4%　on　normal　data,　98.6%　on　DoS　attack,　93.9%　on　Probe　attack,　56.1%　on　U2R　attack,　and　77.2%　on　R2L　attack.　©　2006-2016　by　CCC　Publications.