The　proliferation　of　malware　has　presented　a　serious　threat　to　the　security　of　computer　systems.　Traditional　signature-based　anti-virus　systems　fail　to　detect　polymorphic/metamorphic　and　new,　previously　unseen　malicious　executables.　Data　mining　methods　such　as　Naive　Bayes　and　Decision　Tree　have　been　studied　on　small　collections　of　executables.　In　this　paper,　resting　on　the　analysis　of　Windows　APIs　called　by　PE　files,　we　develop　the　Intelligent　Malware　Detection　System　(IMDS)　using　Objective-Oriented　Association　(OOA)　mining　based　classification.　IMDS　is　an　integrated　system　consisting　of　three　major　modules:　PE　parser,　OOA　rule　generator,　and　rule　based　classifier.　An　OOA_Fast_FP-Growth　algorithm　is　adapted　to　efficiently　generate　OOA　rules　for　classification.　A　comprehensive　experimental　study　on　a　large　collection　of　PE　files　obtained　from　the　anti-virus　laboratory　of　KingSoft　Corporation　is　performed　to　compare　various　malware　detection　approaches.　Promising　experimental　results　demonstrate　that　the　accuracy　and　efficiency　of　our　IMDS　system　outperform　popular　anti-virus　software　such　as　Norton　AntiVirus　and　McAfee　VirusScan,　as　well　as　previous　data　mining　based　detection　systems　which　employed　Naive　Bayes,　Support　Vector　Machine　(SVM)　and　Decision　Tree　techniques.　Our　system　has　already　been　incorporated　into　the　scanning　tool　of　KingSoft＇s　Anti-Virus　software.　©　2008　Springer-Verlag　France.